# FILE: retrieval/indexer.py
# De-identify -> embed dense/sparse -> upsert to Qdrant with payload

import json
import logging
import re
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
from typing import Any

import numpy as np
import spacy
import torch
import yaml
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, PointStruct, SparseVector, VectorParams
from sentence_transformers import SentenceTransformer

from .chunker import DocumentChunker
from .pii_detector import PIIDetector, PIIRedactor


@dataclass
class IndexingResult:
    collection_name: str
    points_indexed: int
    points_updated: int
    points_failed: int
    processing_time: float
    errors: list[str]


class RAGIndexer:
    def __init__(self, config_path: str, qdrant_url: str = "http://localhost:6333"):
        with open(config_path) as f:
            self.config = yaml.safe_load(f)

        self.qdrant_client = QdrantClient(url=qdrant_url)
        self.chunker = DocumentChunker(config_path)
        self.pii_detector = PIIDetector()
        self.pii_redactor = PIIRedactor()

        # Initialize embedding models
        self.dense_model = SentenceTransformer(
            self.config.get("embedding_model", "bge-small-en-v1.5")
        )

        # Initialize sparse model (BM25/SPLADE)
        self.sparse_model = self._init_sparse_model()

        # Initialize NLP pipeline
        self.nlp = spacy.load("en_core_web_sm")

        self.logger = logging.getLogger(__name__)

    def _init_sparse_model(self):
        """Initialize sparse embedding model (BM25 or SPLADE)"""
        sparse_config = self.config.get("sparse_model", {})
        model_type = sparse_config.get("type", "bm25")

        if model_type == "bm25":
            from rank_bm25 import BM25Okapi

            return BM25Okapi
        elif model_type == "splade":
            from transformers import AutoModelForMaskedLM, AutoTokenizer

            tokenizer = AutoTokenizer.from_pretrained(
                "naver/splade-cocondenser-ensembledistil"
            )
            model = AutoModelForMaskedLM.from_pretrained(
                "naver/splade-cocondenser-ensembledistil"
            )
            return {"tokenizer": tokenizer, "model": model}
        else:
            raise ValueError(f"Unsupported sparse model type: {model_type}")

    async def index_document(
        self, document_path: str, collection_name: str, metadata: dict[str, Any]
    ) -> IndexingResult:
        """Index a single document into the specified collection"""
        start_time = datetime.now()
        errors = []
        points_indexed = 0
        points_updated = 0
        points_failed = 0

        try:
            # Step 1: Chunk the document
            chunks = await self.chunker.chunk_document(document_path, metadata)

            # Step 2: Process each chunk
            points = []
            for chunk in chunks:
                try:
                    point = await self._process_chunk(chunk, collection_name, metadata)
                    if point:
                        points.append(point)
                except Exception as e:
                    self.logger.error(
                        f"Failed to process chunk {chunk.get('id', 'unknown')}: {str(e)}"
                    )
                    errors.append(f"Chunk processing error: {str(e)}")
                    points_failed += 1

            # Step 3: Upsert to Qdrant
            if points:
                try:
                    operation_info = self.qdrant_client.upsert(
                        collection_name=collection_name, points=points, wait=True
                    )
                    points_indexed = len(points)
                    self.logger.info(
                        f"Indexed {points_indexed} points to {collection_name}"
                    )
                except Exception as e:
                    self.logger.error(f"Failed to upsert to Qdrant: {str(e)}")
                    errors.append(f"Qdrant upsert error: {str(e)}")
                    points_failed += len(points)
                    points_indexed = 0

        except Exception as e:
            self.logger.error(f"Document indexing failed: {str(e)}")
            errors.append(f"Document indexing error: {str(e)}")

        processing_time = (datetime.now() - start_time).total_seconds()

        return IndexingResult(
            collection_name=collection_name,
            points_indexed=points_indexed,
            points_updated=points_updated,
            points_failed=points_failed,
            processing_time=processing_time,
            errors=errors,
        )

    async def _process_chunk(
        self, chunk: dict[str, Any], collection_name: str, base_metadata: dict[str, Any]
    ) -> PointStruct | None:
        """Process a single chunk: de-identify, embed, create point"""

        # Step 1: De-identify PII
        content = chunk["content"]
        pii_detected = self.pii_detector.detect(content)

        if pii_detected:
            # Redact PII and create mapping
            redacted_content, pii_mapping = self.pii_redactor.redact(
                content, pii_detected
            )

            # Store PII mapping securely (not in vector DB)
            await self._store_pii_mapping(chunk["id"], pii_mapping)

            # Log PII detection for audit
            self.logger.warning(
                f"PII detected in chunk {chunk['id']}: {[p['type'] for p in pii_detected]}"
            )
        else:
            redacted_content = content

        # Verify no PII remains
        if not self._verify_pii_free(redacted_content):
            self.logger.error(f"PII verification failed for chunk {chunk['id']}")
            return None

        # Step 2: Generate embeddings
        try:
            dense_vector = await self._generate_dense_embedding(redacted_content)
            sparse_vector = await self._generate_sparse_embedding(redacted_content)
        except Exception as e:
            self.logger.error(
                f"Embedding generation failed for chunk {chunk['id']}: {str(e)}"
            )
            return None

        # Step 3: Prepare metadata
        payload = self._prepare_payload(chunk, base_metadata, redacted_content)
        payload["pii_free"] = True  # Verified above

        # Step 4: Create point
        point = PointStruct(
            id=chunk["id"],
            vector={"dense": dense_vector, "sparse": sparse_vector},
            payload=payload,
        )

        return point

    async def _generate_dense_embedding(self, text: str) -> list[float]:
        """Generate dense vector embedding"""
        try:
            # Use sentence transformer for dense embeddings
            embedding = self.dense_model.encode(text, normalize_embeddings=True)
            return embedding.tolist()
        except Exception as e:
            self.logger.error(f"Dense embedding generation failed: {str(e)}")
            raise

    async def _generate_sparse_embedding(self, text: str) -> SparseVector:
        """Generate sparse vector embedding (BM25 or SPLADE)"""
        vector = SparseVector(indices=[], values=[])

        try:
            sparse_config = self.config.get("sparse_model", {})
            model_type = sparse_config.get("type", "bm25")

            if model_type == "bm25":
                # Simple BM25-style sparse representation
                doc = self.nlp(text)
                tokens = [
                    token.lemma_.lower()
                    for token in doc
                    if not token.is_stop and not token.is_punct
                ]

                # Create term frequency vector
                term_freq = {}
                for token in tokens:
                    term_freq[token] = term_freq.get(token, 0) + 1

                # Convert to sparse vector format
                vocab_size = sparse_config.get("vocab_size", 30000)
                indices = []
                values = []

                for term, freq in term_freq.items():
                    # Simple hash-based vocabulary mapping
                    term_id = hash(term) % vocab_size
                    indices.append(term_id)
                    values.append(float(freq))

                vector = SparseVector(indices=indices, values=values)

            elif model_type == "splade":
                # SPLADE sparse embeddings
                tokenizer = self.sparse_model["tokenizer"]
                model = self.sparse_model["model"]

                inputs = tokenizer(
                    text, return_tensors="pt", truncation=True, max_length=512
                )
                outputs = model(**inputs)

                # Extract sparse representation
                logits = outputs.logits.squeeze()
                sparse_rep = torch.relu(logits).detach().numpy()

                # Convert to sparse format
                indices = np.nonzero(sparse_rep)[0].tolist()
                values = sparse_rep[indices].tolist()

                vector = SparseVector(indices=indices, values=values)

            return vector

        except Exception as e:
            self.logger.error(f"Sparse embedding generation failed: {str(e)}")
            # Return empty sparse vector as fallback
            return vector

    def _prepare_payload(
        self, chunk: dict[str, Any], base_metadata: dict[str, Any], content: str
    ) -> dict[str, Any]:
        """Prepare payload metadata for the chunk"""

        # Start with base metadata
        payload = base_metadata.copy()

        # Add chunk-specific metadata
        payload.update(
            {
                "document_id": chunk.get("document_id"),
                "content": content,  # De-identified content
                "chunk_index": chunk.get("chunk_index", 0),
                "total_chunks": chunk.get("total_chunks", 1),
                "page_numbers": chunk.get("page_numbers", []),
                "section_hierarchy": chunk.get("section_hierarchy", []),
                "has_calculations": self._detect_calculations(content),
                "has_forms": self._detect_form_references(content),
                "confidence_score": chunk.get("confidence_score", 1.0),
                "created_at": datetime.now().isoformat(),
                "version": self.config.get("version", "1.0"),
            }
        )

        # Extract and add topic tags
        topic_tags = self._extract_topic_tags(content)
        if topic_tags:
            payload["topic_tags"] = topic_tags

        # Add content analysis
        payload.update(self._analyze_content(content))

        return payload

    def _detect_calculations(self, text: str) -> bool:
        """Detect if text contains calculations or formulas"""
        calculation_patterns = [
            r"\d+\s*[+\-*/]\s*\d+",
            r"£\d+(?:,\d{3})*(?:\.\d{2})?",
            r"\d+(?:\.\d+)?%",
            r"total|sum|calculate|compute",
            r"rate|threshold|allowance|relief",
        ]

        for pattern in calculation_patterns:
            if re.search(pattern, text, re.IGNORECASE):
                return True
        return False

    def _detect_form_references(self, text: str) -> bool:
        """Detect references to tax forms"""
        form_patterns = [
            r"SA\d{3}",
            r"P\d{2}",
            r"CT\d{3}",
            r"VAT\d{3}",
            r"form\s+\w+",
            r"schedule\s+\w+",
        ]

        for pattern in form_patterns:
            if re.search(pattern, text, re.IGNORECASE):
                return True
        return False

    def _extract_topic_tags(self, text: str) -> list[str]:
        """Extract topic tags from content"""
        topic_keywords = {
            "employment": [
                "PAYE",
                "payslip",
                "P60",
                "employment",
                "salary",
                "wages",
                "employer",
            ],
            "self_employment": [
                "self-employed",
                "business",
                "turnover",
                "expenses",
                "profit",
                "loss",
            ],
            "property": ["rental", "property", "landlord", "FHL", "mortgage", "rent"],
            "dividends": ["dividend", "shares", "distribution", "corporation tax"],
            "capital_gains": ["capital gains", "disposal", "acquisition", "CGT"],
            "pensions": ["pension", "retirement", "SIPP", "occupational"],
            "savings": ["interest", "savings", "ISA", "bonds"],
            "inheritance": ["inheritance", "IHT", "estate", "probate"],
            "vat": ["VAT", "value added tax", "registration", "return"],
        }

        tags = []
        text_lower = text.lower()

        for topic, keywords in topic_keywords.items():
            for keyword in keywords:
                if keyword.lower() in text_lower:
                    tags.append(topic)
                    break

        return list(set(tags))  # Remove duplicates

    def _analyze_content(self, text: str) -> dict[str, Any]:
        """Analyze content for additional metadata"""
        doc = self.nlp(text)

        return {
            "word_count": len([token for token in doc if not token.is_space]),
            "sentence_count": len(list(doc.sents)),
            "entity_count": len(doc.ents),
            "complexity_score": self._calculate_complexity(doc),
            "language": doc.lang_ if hasattr(doc, "lang_") else "en",
        }

    def _calculate_complexity(self, doc: dict) -> float:
        """Calculate text complexity score"""
        if not doc:
            return 0.0

        # Simple complexity based on sentence length and vocabulary
        avg_sentence_length = sum(len(sent) for sent in doc.sents) / len(
            list(doc.sents)
        )
        unique_words = len(set(token.lemma_.lower() for token in doc if token.is_alpha))
        total_words = len([token for token in doc if token.is_alpha])

        vocabulary_diversity = unique_words / total_words if total_words > 0 else 0

        # Normalize to 0-1 scale
        complexity = min(1.0, (avg_sentence_length / 20.0 + vocabulary_diversity) / 2.0)
        return complexity

    def _verify_pii_free(self, text: str) -> bool:
        """Verify that text contains no PII"""
        # Quick verification using patterns
        pii_patterns = [
            r"\b[A-Z]{2}\d{6}[A-D]\b",  # NI number
            r"\b\d{10}\b",  # UTR
            r"\b[A-Z]{2}\d{2}[A-Z]{4}\d{14}\b",  # IBAN
            r"\b\d{2}-\d{2}-\d{2}\b",  # Sort code
            r"\b[A-Z]{1,2}\d[A-Z\d]?\s*\d[A-Z]{2}\b",  # Postcode
            r"\b[\w\.-]+@[\w\.-]+\.\w+\b",  # Email
            r"\b(?:\+44|0)\d{10,11}\b",  # Phone
        ]

        for pattern in pii_patterns:
            if re.search(pattern, text):
                return False

        return True

    async def _store_pii_mapping(
        self, chunk_id: str, pii_mapping: dict[str, Any]
    ) -> None:
        """Store PII mapping in secure client data store (not in vector DB)"""
        # This would integrate with the secure PostgreSQL client data store
        # For now, just log the mapping securely
        self.logger.info(
            f"PII mapping stored for chunk {chunk_id}: {len(pii_mapping)} items"
        )

    async def create_collections(self) -> None:
        """Create all Qdrant collections based on configuration"""
        collections_config_path = Path(__file__).parent / "qdrant_collections.json"

        with open(collections_config_path) as f:
            collections_config = json.load(f)

        for collection_config in collections_config["collections"]:
            collection_name = collection_config["name"]

            try:
                # Check if collection exists
                try:
                    self.qdrant_client.get_collection(collection_name)
                    self.logger.info(f"Collection {collection_name} already exists")
                    continue
                except:
                    pass  # Collection doesn't exist, create it

                # Create collection
                vectors_config = {}

                # Dense vector configuration
                if "dense" in collection_config:
                    vectors_config["dense"] = VectorParams(
                        size=collection_config["dense"]["size"],
                        distance=Distance.COSINE,
                    )

                # Sparse vector configuration
                if collection_config.get("sparse", False):
                    vectors_config["sparse"] = VectorParams(
                        size=30000,  # Vocabulary size for sparse vectors
                        distance=Distance.DOT,
                        on_disk=True,
                    )

                self.qdrant_client.create_collection(
                    collection_name=collection_name,
                    vectors_config=vectors_config,
                    **collection_config.get("indexing_config", {}),
                )

                self.logger.info(f"Created collection: {collection_name}")

            except Exception as e:
                self.logger.error(
                    f"Failed to create collection {collection_name}: {str(e)}"
                )
                raise

    async def batch_index(
        self, documents: list[dict[str, Any]], collection_name: str
    ) -> list[IndexingResult]:
        """Index multiple documents in batch"""
        results = []

        for doc_info in documents:
            result = await self.index_document(
                doc_info["path"], collection_name, doc_info["metadata"]
            )
            results.append(result)

        return results

    def get_collection_stats(self, collection_name: str) -> dict[str, Any]:
        """Get statistics for a collection"""
        try:
            collection_info = self.qdrant_client.get_collection(collection_name)
            return {
                "name": collection_name,
                "vectors_count": collection_info.vectors_count,
                "indexed_vectors_count": collection_info.indexed_vectors_count,
                "points_count": collection_info.points_count,
                "segments_count": collection_info.segments_count,
                "status": collection_info.status,
            }
        except Exception as e:
            self.logger.error(f"Failed to get stats for {collection_name}: {str(e)}")
            return {"error": str(e)}