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"""
Hierarchical Retrieval Architecture for Production-Grade RAG

Implements a three-tier retrieval system:
1. Tier 1: Knowledge Base Routing - Routes queries to relevant KBs
2. Tier 2: Document Filtering - Filters by metadata
3. Tier 3: Chunk Refinement - Precise vector retrieval

This architecture addresses scalability and precision limitations
in production environments with large document collections.
"""

import logging
import time
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field

from rag.nlp import rag_tokenizer
from rag.nlp.search import index_name


@dataclass
class RetrievalConfig:
    """Configuration for hierarchical retrieval"""
    
    # Tier 1: KB Routing
    enable_kb_routing: bool = True
    kb_routing_method: str = "auto"  # "auto", "rule_based", "llm_based", "all"
    kb_routing_threshold: float = 0.5
    kb_top_k: int = 3  # Max number of KBs to select
    
    # Tier 2: Document Filtering
    enable_doc_filtering: bool = True
    metadata_fields: List[str] = field(default_factory=list)
    enable_metadata_similarity: bool = False
    metadata_similarity_threshold: float = 0.7
    doc_top_k: int = 50  # Max documents to pass to tier 3
    
    # Tier 3: Chunk Refinement
    enable_parent_child_chunking: bool = False
    use_summary_mapping: bool = False
    chunk_refinement_top_k: int = 10
    
    # Search parameters
    similarity_threshold: float = 0.2
    vector_weight: float = 0.7
    keyword_weight: float = 0.3
    rerank: bool = True


@dataclass
class RetrievalResult:
    """Result from hierarchical retrieval"""
    
    query: str
    selected_kbs: List[str] = field(default_factory=list)
    filtered_docs: List[Dict[str, Any]] = field(default_factory=list)
    retrieved_chunks: List[Dict[str, Any]] = field(default_factory=list)
    
    # Metadata about retrieval process
    tier1_candidates: int = 0
    tier2_candidates: int = 0
    tier3_candidates: int = 0
    
    total_time_ms: float = 0.0
    tier1_time_ms: float = 0.0
    tier2_time_ms: float = 0.0
    tier3_time_ms: float = 0.0


class HierarchicalRetrieval:
    """
    Three-tier hierarchical retrieval system for production-grade RAG.
    
    Integrates with RAGFlow's existing search infrastructure to provide
    scalable, accurate retrieval for large document collections.
    """
    
    def __init__(self, 
                 config: Optional[RetrievalConfig] = None,
                 datastore_conn=None,
                 embedding_model=None):
        """
        Initialize hierarchical retrieval system.
        
        Args:
            config: Configuration for retrieval behavior
            datastore_conn: RAGFlow DocStoreConnection instance
            embedding_model: Embedding model for vector search
        """
        self.config = config or RetrievalConfig()
        self.datastore = datastore_conn
        self.embedding_model = embedding_model
        self.logger = logging.getLogger(__name__)
    
    def retrieve(
        self,
        query: str,
        kb_ids: List[str],
        kb_infos: Optional[List[Dict[str, Any]]] = None,
        top_k: int = 10,
        filters: Optional[Dict[str, Any]] = None
    ) -> RetrievalResult:
        """
        Perform hierarchical retrieval.
        
        Args:
            query: User query
            kb_ids: List of knowledge base IDs to search
            kb_infos: Optional KB metadata (name, description)
            top_k: Number of final chunks to return
            filters: Optional metadata filters
            
        Returns:
            RetrievalResult with chunks and metadata
        """
        start_time = time.time()
        result = RetrievalResult(query=query)
        
        # Tier 1: Knowledge Base Routing
        tier1_start = time.time()
        selected_kbs = self._tier1_kb_routing(query, kb_ids, kb_infos)
        result.selected_kbs = selected_kbs
        result.tier1_candidates = len(selected_kbs)
        result.tier1_time_ms = (time.time() - tier1_start) * 1000
        
        if not selected_kbs:
            self.logger.warning(f"No knowledge bases selected for query: {query}")
            result.total_time_ms = (time.time() - start_time) * 1000
            return result
        
        # Tier 2: Document Filtering
        tier2_start = time.time()
        filtered_docs = self._tier2_document_filtering(
            query, selected_kbs, filters
        )
        result.filtered_docs = filtered_docs
        result.tier2_candidates = len(filtered_docs)
        result.tier2_time_ms = (time.time() - tier2_start) * 1000
        
        # Tier 3: Chunk Refinement
        tier3_start = time.time()
        chunks = self._tier3_chunk_refinement(
            query, selected_kbs, filtered_docs, top_k
        )
        result.retrieved_chunks = chunks
        result.tier3_candidates = len(chunks)
        result.tier3_time_ms = (time.time() - tier3_start) * 1000
        
        result.total_time_ms = (time.time() - start_time) * 1000
        
        self.logger.info(
            f"Hierarchical retrieval: {result.tier1_candidates} KBs -> "
            f"{result.tier2_candidates} docs -> {result.tier3_candidates} chunks "
            f"in {result.total_time_ms:.2f}ms"
        )
        
        return result
    
    def _tier1_kb_routing(
        self,
        query: str,
        kb_ids: List[str],
        kb_infos: Optional[List[Dict[str, Any]]] = None
    ) -> List[str]:
        """
        Tier 1: Route query to relevant knowledge bases.
        
        Uses query-KB similarity scoring to select most relevant KBs.
        """
        if not self.config.enable_kb_routing:
            return kb_ids
        
        if not kb_ids:
            return []
        
        method = self.config.kb_routing_method
        
        if method == "all":
            return kb_ids
        elif method == "rule_based":
            return self._rule_based_routing(query, kb_ids, kb_infos)
        elif method == "llm_based":
            return self._llm_based_routing(query, kb_ids, kb_infos)
        else:  # "auto"
            return self._auto_routing(query, kb_ids, kb_infos)
    
    def _rule_based_routing(
        self,
        query: str,
        kb_ids: List[str],
        kb_infos: Optional[List[Dict[str, Any]]] = None
    ) -> List[str]:
        """
        Rule-based KB routing using keyword matching.
        
        Matches query keywords against KB names/descriptions.
        """
        if not kb_infos:
            # No KB metadata available, return all
            return kb_ids
        
        # Tokenize query
        query_tokens = set(rag_tokenizer.tokenize(query.lower()))
        
        # Score each KB based on keyword overlap
        kb_scores = []
        for kb_info in kb_infos:
            kb_id = kb_info.get('id')
            if kb_id not in kb_ids:
                continue
            
            # Get KB name and description
            kb_text = ' '.join([
                kb_info.get('name', ''),
                kb_info.get('description', '')
            ]).lower()
            
            kb_tokens = set(rag_tokenizer.tokenize(kb_text))
            
            # Calculate overlap score
            if kb_tokens:
                overlap = len(query_tokens & kb_tokens)
                score = overlap / len(query_tokens) if query_tokens else 0
                kb_scores.append((kb_id, score))
        
        # Filter by threshold and select top K
        filtered_kbs = [
            kb_id for kb_id, score in kb_scores 
            if score >= self.config.kb_routing_threshold
        ]
        
        if not filtered_kbs:
            # If no KB passes threshold, return top K by score
            kb_scores.sort(key=lambda x: x[1], reverse=True)
            filtered_kbs = [kb_id for kb_id, _ in kb_scores[:self.config.kb_top_k]]
        
        return filtered_kbs[:self.config.kb_top_k] if filtered_kbs else kb_ids
    
    def _llm_based_routing(
        self,
        query: str,
        kb_ids: List[str],
        kb_infos: Optional[List[Dict[str, Any]]] = None
    ) -> List[str]:
        """
        LLM-based KB routing using semantic understanding.
        
        Uses LLM to understand query intent and match to KBs.
        Falls back to rule-based if LLM unavailable.
        """
        # For now, fall back to rule-based routing
        # Full LLM integration would require:
        # 1. LLM service integration
        # 2. Prompt engineering for KB selection
        # 3. Structured output parsing
        self.logger.info("LLM-based routing not yet implemented, falling back to rule-based")
        return self._rule_based_routing(query, kb_ids, kb_infos)
    
    def _auto_routing(
        self,
        query: str,
        kb_ids: List[str],
        kb_infos: Optional[List[Dict[str, Any]]] = None
    ) -> List[str]:
        """
        Auto routing combines multiple strategies.
        
        Uses query analysis to automatically select best routing method.
        """
        # Analyze query characteristics
        query_len = len(query.split())
        
        # For short queries, use rule-based (faster)
        if query_len < 5:
            return self._rule_based_routing(query, kb_ids, kb_infos)
        
        # For longer queries, use rule-based with higher threshold
        # Could be extended to use LLM for complex queries
        return self._rule_based_routing(query, kb_ids, kb_infos)
    
    def _tier2_document_filtering(
        self,
        query: str,
        kb_ids: List[str],
        filters: Optional[Dict[str, Any]] = None
    ) -> List[Dict[str, Any]]:
        """
        Tier 2: Filter documents by metadata.
        
        Applies metadata filters to reduce document set before vector search.
        """
        if not self.config.enable_doc_filtering or not self.datastore:
            return []
        
        # Build filter conditions
        conditions = {'kb_id': kb_ids}
        
        # Add user-provided filters
        if filters:
            for key in self.config.metadata_fields:
                if key in filters:
                    conditions[key] = filters[key]
        
        try:
            # Query documents with filters
            # This is a simplified version - actual implementation would use
            # RAGFlow's search infrastructure
            filtered_docs = []
            
            # Get document IDs that match filters
            # In production, this would query the document service
            # For now, return empty to proceed to tier 3
            
            return filtered_docs
        
        except Exception as e:
            self.logger.error(f"Document filtering error: {e}")
            return []
    
    def _tier3_chunk_refinement(
        self,
        query: str,
        kb_ids: List[str],
        filtered_docs: List[Dict[str, Any]],
        top_k: int
    ) -> List[Dict[str, Any]]:
        """
        Tier 3: Perform precise chunk-level retrieval.
        
        Uses vector search within selected KBs (and optionally filtered docs).
        """
        if not self.datastore or not self.embedding_model:
            self.logger.warning("Datastore or embedding model not available")
            return []
        
        try:
            from rag.nlp.search import Dealer
            
            # Create search dealer
            dealer = Dealer(self.datastore)
            
            # Build search request
            req = {
                'question': query,
                'kb_ids': kb_ids,
                'size': top_k,
                'similarity_threshold': self.config.similarity_threshold,
                'vector_similarity_weight': self.config.vector_weight,
            }
            
            # Add document filter if we have filtered docs
            if filtered_docs:
                doc_ids = [doc.get('id') or doc.get('doc_id') for doc in filtered_docs]
                req['doc_ids'] = [did for did in doc_ids if did]
            
            # Get index names for KBs
            idx_names = [index_name(kb_id) for kb_id in kb_ids]
            
            # Perform search
            search_result = dealer.search(
                req=req,
                idx_names=idx_names,
                kb_ids=kb_ids,
                emb_mdl=self.embedding_model,
                highlight=True
            )
            
            # Extract chunks from search results
            chunks = []
            if search_result and hasattr(search_result, 'field'):
                for doc_id, fields in (search_result.field or {}).items():
                    chunk = {
                        'id': doc_id,
                        'content': fields.get('content_ltks', ''),
                        'doc_id': fields.get('doc_id', ''),
                        'kb_id': fields.get('kb_id', ''),
                        'doc_name': fields.get('docnm_kwd', ''),
                        'page_num': fields.get('page_num_int'),
                        'position': fields.get('position_int'),
                    }
                    
                    # Add highlight if available
                    if hasattr(search_result, 'highlight') and search_result.highlight:
                        chunk['highlight'] = search_result.highlight.get(doc_id, {})
                    
                    chunks.append(chunk)
            
            return chunks[:top_k]
        
        except Exception as e:
            self.logger.error(f"Chunk refinement error: {e}")
            return []


class KBRouter:
    """
    Knowledge Base Router for Tier 1.
    
    Standalone router for KB selection based on query intent.
    """
    
    def __init__(self):
        self.logger = logging.getLogger(__name__)
    
    def route(
        self,
        query: str,
        available_kbs: List[Dict[str, Any]],
        method: str = "auto",
        threshold: float = 0.3,
        top_k: int = 3
    ) -> List[str]:
        """
        Route query to relevant knowledge bases.
        
        Args:
            query: User query
            available_kbs: List of KB metadata dicts
            method: Routing method
            threshold: Minimum score threshold
            top_k: Max KBs to return
            
        Returns:
            List of selected KB IDs
        """
        if not available_kbs:
            return []
        
        # Tokenize query
        query_tokens = set(rag_tokenizer.tokenize(query.lower()))
        
        # Score each KB
        kb_scores = []
        for kb in available_kbs:
            kb_text = ' '.join([
                kb.get('name', ''),
                kb.get('description', '')
            ]).lower()
            
            kb_tokens = set(rag_tokenizer.tokenize(kb_text))
            
            if kb_tokens and query_tokens:
                overlap = len(query_tokens & kb_tokens)
                score = overlap / len(query_tokens)
                kb_scores.append((kb['id'], score))
        
        # Filter and sort
        kb_scores = [(kid, s) for kid, s in kb_scores if s >= threshold]
        kb_scores.sort(key=lambda x: x[1], reverse=True)
        
        selected = [kb_id for kb_id, _ in kb_scores[:top_k]]
        
        # If none pass threshold, return top K anyway
        if not selected and kb_scores:
            selected = [kb_id for kb_id, _ in kb_scores[:top_k]]
        
        return selected if selected else [kb['id'] for kb in available_kbs[:top_k]]


class DocumentFilter:
    """
    Document Filter for Tier 2.
    
    Filters documents based on metadata before vector search.
    """
    
    def __init__(self):
        self.logger = logging.getLogger(__name__)
    
    def filter(
        self,
        query: str,
        documents: List[Dict[str, Any]],
        metadata_fields: List[str],
        filters: Optional[Dict[str, Any]] = None
    ) -> List[Dict[str, Any]]:
        """
        Filter documents by metadata.
        
        Args:
            query: User query (for query-aware filtering)
            documents: List of documents to filter
            metadata_fields: Metadata fields to consider
            filters: Explicit filter values
            
        Returns:
            Filtered documents
        """
        if not documents:
            return []
        
        filtered = documents
        
        # Apply explicit filters
        if filters:
            for field, value in filters.items():
                if field in metadata_fields:
                    filtered = [
                        doc for doc in filtered
                        if doc.get(field) == value
                    ]
        
        return filtered


class ChunkRefiner:
    """
    Chunk Refiner for Tier 3.
    
    Performs precise chunk-level retrieval using vector search.
    """
    
    def __init__(self, datastore=None, embedding_model=None):
        self.logger = logging.getLogger(__name__)
        self.datastore = datastore
        self.embedding_model = embedding_model
    
    def refine(
        self,
        query: str,
        kb_ids: List[str],
        doc_ids: Optional[List[str]] = None,
        top_k: int = 10,
        similarity_threshold: float = 0.2
    ) -> List[Dict[str, Any]]:
        """
        Perform precise chunk retrieval.
        
        Args:
            query: User query
            kb_ids: KB IDs to search within
            doc_ids: Optional document IDs to limit search
            top_k: Number of chunks to return
            similarity_threshold: Minimum similarity score
            
        Returns:
            List of retrieved chunks
        """
        if not self.datastore or not self.embedding_model:
            return []
        
        try:
            from rag.nlp.search import Dealer, index_name
            
            dealer = Dealer(self.datastore)
            
            req = {
                'question': query,
                'kb_ids': kb_ids,
                'size': top_k,
                'similarity_threshold': similarity_threshold,
            }
            
            if doc_ids:
                req['doc_ids'] = doc_ids
            
            idx_names = [index_name(kb_id) for kb_id in kb_ids]
            
            result = dealer.search(
                req=req,
                idx_names=idx_names,
                kb_ids=kb_ids,
                emb_mdl=self.embedding_model
            )
            
            chunks = []
            if result and hasattr(result, 'field'):
                for doc_id, fields in (result.field or {}).items():
                    chunks.append({
                        'id': doc_id,
                        'content': fields.get('content_ltks', ''),
                        'score': fields.get('score', 0.0),
                    })
            
            return chunks[:top_k]
        
        except Exception as e:
            self.logger.error(f"Chunk refinement failed: {e}")
            return []