utils.py

import logging
import sys
from llama_index.llms.google_genai import GoogleGenAI
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from sentence_transformers import CrossEncoder
from config import AVAILABLE_MODELS, DEFAULT_MODEL, GOOGLE_API_KEY
import time
from index_retriever import rerank_nodes
from my_logging import log_message
from config import PROMPT_SIMPLE_POISK

def get_llm_model(model_name):
    try:
        model_config = AVAILABLE_MODELS.get(model_name)
        if not model_config:
            log_message(f"Модель {model_name} не найдена, использую модель по умолчанию")
            model_config = AVAILABLE_MODELS[DEFAULT_MODEL]
        
        if not model_config.get("api_key"):
            raise Exception(f"API ключ не найден для модели {model_name}")
        
        if model_config["provider"] == "google":
            return GoogleGenAI(
                model=model_config["model_name"], 
                api_key=model_config["api_key"]
            )
        elif model_config["provider"] == "openai":
            return OpenAI(
                model=model_config["model_name"],
                api_key=model_config["api_key"]
            )
        else:
            raise Exception(f"Неподдерживаемый провайдер: {model_config['provider']}")
            
    except Exception as e:
        log_message(f"Ошибка создания модели {model_name}: {str(e)}")
        return GoogleGenAI(model="gemini-2.0-flash", api_key=GOOGLE_API_KEY)

def get_embedding_model(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"):
    return HuggingFaceEmbedding(model_name=model_name)

def get_reranker_model(model_name='cross-encoder/ms-marco-MiniLM-L-12-v2'):
    return CrossEncoder(model_name)

def generate_sources_html(nodes, chunks_df=None):
    html = "<div style='background-color: #2d3748; color: white; padding: 20px; border-radius: 10px; max-height: 400px; overflow-y: auto;'>"
    html += "<h3 style='color: #63b3ed; margin-top: 0;'>Источники:</h3>"
    
    sources_by_doc = {}
    
    for i, node in enumerate(nodes):
        metadata = node.metadata if hasattr(node, 'metadata') else {}
        doc_type = metadata.get('type', 'text')
        doc_id = metadata.get('document_id', 'unknown')
        
        if doc_type == 'table' or doc_type == 'table_row':
            table_num = metadata.get('table_number', 'unknown')
            key = f"{doc_id}_table_{table_num}"
        elif doc_type == 'image':
            image_num = metadata.get('image_number', 'unknown')
            key = f"{doc_id}_image_{image_num}"
        else:
            section_path = metadata.get('section_path', '')
            section_id = metadata.get('section_id', '')
            section_key = section_path if section_path else section_id
            key = f"{doc_id}_text_{section_key}"
        
        if key not in sources_by_doc:
            sources_by_doc[key] = {
                'doc_id': doc_id,
                'doc_type': doc_type,
                'metadata': metadata,
                'sections': set()
            }
        
        if doc_type not in ['table', 'table_row', 'image']:
            section_path = metadata.get('section_path', '')
            section_id = metadata.get('section_id', '')
            if section_path:
                sources_by_doc[key]['sections'].add(f"пункт {section_path}")
            elif section_id and section_id != 'unknown':
                sources_by_doc[key]['sections'].add(f"пункт {section_id}")
    
    for source_info in sources_by_doc.values():
        metadata = source_info['metadata']
        doc_type = source_info['doc_type']
        doc_id = source_info['doc_id']
        
        html += f"<div style='margin-bottom: 15px; padding: 15px; border: 1px solid #4a5568; border-radius: 8px; background-color: #1a202c;'>"
        
        if doc_type == 'text':
            html += f"<h4 style='margin: 0 0 10px 0; color: #63b3ed;'>📄 {doc_id}</h4>"
        elif doc_type == 'table' or doc_type == 'table_row':
            table_num = metadata.get('table_number', 'unknown')
            table_title = metadata.get('table_title', '')
            if table_num and table_num != 'unknown':
                if not str(table_num).startswith('№'):
                    table_num = f"№{table_num}"
                html += f"<h4 style='margin: 0 0 10px 0; color: #68d391;'>📊 Таблица {table_num} - {doc_id}</h4>"
                if table_title and table_title != 'unknown':
                    html += f"<p style='margin: 5px 0; color: #a0aec0; font-size: 14px;'>{table_title}</p>"
            else:
                html += f"<h4 style='margin: 0 0 10px 0; color: #68d391;'>📊 Таблица - {doc_id}</h4>"
        elif doc_type == 'image':
            image_num = metadata.get('image_number', 'unknown')
            image_title = metadata.get('image_title', '')
            if image_num and image_num != 'unknown':
                if not str(image_num).startswith('№'):
                    image_num = f"№{image_num}"
                html += f"<h4 style='margin: 0 0 10px 0; color: #fbb6ce;'>🖼️ Изображение {image_num} - {doc_id}</h4>"
                if image_title and image_title != 'unknown':
                    html += f"<p style='margin: 5px 0; color: #a0aec0; font-size: 14px;'>{image_title}</p>"
        
        if chunks_df is not None and 'file_link' in chunks_df.columns and doc_type == 'text':
            doc_rows = chunks_df[chunks_df['document_id'] == doc_id]
            if not doc_rows.empty:
                file_link = doc_rows.iloc[0]['file_link']
                html += f"<a href='{file_link}' target='_blank' style='color: #68d391; text-decoration: none; font-size: 14px; display: inline-block; margin-top: 10px;'>🔗 Ссылка на документ</a><br>"
        
        html += "</div>"
    
    html += "</div>"
    return html

def deduplicate_nodes(nodes):
    """Deduplicate retrieved nodes based on content and metadata"""
    seen = set()
    unique_nodes = []
    
    for node in nodes:
        doc_id = node.metadata.get('document_id', '')
        node_type = node.metadata.get('type', 'text')
        
        if node_type == 'table' or node_type == 'table_row':
            table_num = node.metadata.get('table_number', '')
            table_identifier = node.metadata.get('table_identifier', table_num)
            
            # Use row range to distinguish table chunks
            row_start = node.metadata.get('row_start', '')
            row_end = node.metadata.get('row_end', '')
            is_complete = node.metadata.get('is_complete_table', False)
            
            if is_complete:
                identifier = f"{doc_id}|table|{table_identifier}|complete"
            elif row_start != '' and row_end != '':
                identifier = f"{doc_id}|table|{table_identifier}|rows_{row_start}_{row_end}"
            else:
                # Fallback: use chunk_id if available
                chunk_id = node.metadata.get('chunk_id', '')
                if chunk_id != '':
                    identifier = f"{doc_id}|table|{table_identifier}|chunk_{chunk_id}"
                else:
                    # Last resort: hash first 100 chars of content
                    import hashlib
                    content_hash = hashlib.md5(node.text[:100].encode()).hexdigest()[:8]
                    identifier = f"{doc_id}|table|{table_identifier}|{content_hash}"
                    
        elif node_type == 'image':
            img_num = node.metadata.get('image_number', '')
            identifier = f"{doc_id}|image|{img_num}"
            
        else:  # text
            section_id = node.metadata.get('section_id', '')
            chunk_id = node.metadata.get('chunk_id', 0)
            # For text, section_id + chunk_id should be unique
            identifier = f"{doc_id}|text|{section_id}|{chunk_id}"
        
        if identifier not in seen:
            seen.add(identifier)
            unique_nodes.append(node)
    
    return unique_nodes

def debug_search_tables(vector_index, search_term="С-25"):
    """Debug function to find all tables containing a specific term"""
    all_nodes = list(vector_index.docstore.docs.values())
    
    matching = []
    for node in all_nodes:
        if node.metadata.get('type') == 'table':
            text = node.get_content()
            if search_term in text or search_term in node.metadata.get('table_title', ''):
                matching.append({
                    'doc_id': node.metadata.get('document_id'),
                    'table_num': node.metadata.get('table_number'),
                    'title': node.metadata.get('table_title', '')[:100]
                })
    
    log_message(f"\n{'='*60}")
    log_message(f"DEBUG: Found {len(matching)} tables containing '{search_term}'")
    for m in matching:
        log_message(f"  • {m['doc_id']} - Table {m['table_num']}: {m['title']}")
    log_message(f"{'='*60}\n")
    
    return matching

from config import QUERY_EXPANSION_PROMPT
from documents_prep import normalize_text, normalize_steel_designations

KEYWORD_EXPANSIONS = {
    "08X18H10T": ["Листы", "Трубы", "Поковки", "Крепежные изделия", "Сортовой прокат", "Отливки"],
    "12X18H10T": ["Листы", "Поковки", "Сортовой прокат"],
    "10X17H13M2T": ["Трубы", "Арматура", "Поковки", "Фланцы"],
    "20X23H18": ["Листы", "Сортовой прокат", "Поковки"],
    "03X17H14M3": ["Трубы", "Листы", "Проволока"],
    "59023.6": ["Режимы термической обработки стали 59023.6"],
    "СВ-08X19H10": ["Сварочная проволока", "Сварка", "Сварочные материалы"],
}

def enhance_query_with_keywords(query):
    query_upper = query.upper()
    
    # Find matching keywords
    added_context = []
    keywords_found = []
    
    for keyword, expansions in KEYWORD_EXPANSIONS.items():
        keyword_upper = keyword.upper()
        
        # Check if keyword is in query (case-insensitive)
        if keyword_upper in query_upper:
            context = ' '.join(expansions)
            added_context.append(context)
            keywords_found.append(keyword)
            log_message(f"  Found keyword '{keyword}': added context '{context}'")
    
    # Build enhanced query
    if added_context:
        unique_context = ' '.join(set(' '.join(added_context).split()))
        enhanced = f"{query} {unique_context}"
        
        log_message(f"Enhanced query with keywords: {', '.join(keywords_found)}")
        log_message(f"Added context: {unique_context[:100]}...")
        
        return enhanced
    return f"{query}"


def answer_question(question, query_engine, reranker, current_model, chunks_df=None, rerank_top_k=20):
    
    normalized_question = normalize_text(question)
    normalized_question_2, query_changes, change_list = normalize_steel_designations(question)
    
    # Step 1: Keyword-based enhancement
    enhanced_question = enhance_query_with_keywords(normalized_question_2)
    
    # Step 2: LLM-based query expansion
    try:
        llm = get_llm_model(current_model)
        expansion_prompt = QUERY_EXPANSION_PROMPT.format(original_query=enhanced_question)
        expanded_queries = llm.complete(expansion_prompt).text.strip()
        enhanced_question = f"{enhanced_question} {expanded_queries}"
        log_message(f"LLM expanded query: {expanded_queries[:200]}...")
    except Exception as e:
        log_message(f"Query expansion failed: {e}, using keyword-only enhancement")
    
    if change_list:
        log_message(f"Query changes: {', '.join(change_list)}")
    if change_list:
        log_message(f"Query changes: {', '.join(change_list)}")    
    if query_engine is None:
        return "<div style='background-color: #e53e3e; color: white; padding: 20px; border-radius: 10px;'>Система не инициализирована</div>", "", ""
    
    try:
        start_time = time.time()
        retrieved_nodes = query_engine.retriever.retrieve(enhanced_question)
        log_message(f"user query: {question}")
        log_message(f"normalized query: {normalized_question}")
        log_message(f"after steel normalization: {normalized_question_2}")
        log_message(f"enhanced query: {enhanced_question}") 
        log_message(f"Steel grades normalized in query: {query_changes}")
        
        log_message(f"RETRIEVED: {len(retrieved_nodes)} nodes")
        
        unique_retrieved = deduplicate_nodes(retrieved_nodes)

        # IMPROVED DEBUG: Log what was actually retrieved with FULL metadata
        log_message(f"RETRIEVED: unique {len(unique_retrieved)} nodes")
        for i, node in enumerate(unique_retrieved):
            node_type = node.metadata.get('type', 'text')
            doc_id = node.metadata.get('document_id', 'N/A')
            
            if node_type == 'table':
                table_num = node.metadata.get('table_number', 'N/A')
                table_id = node.metadata.get('table_identifier', 'N/A')
                table_title = node.metadata.get('table_title', 'N/A')
                # Show first 200 chars of content to verify it's the right table
                content_preview = node.text[:200].replace('\n', ' ')
                log_message(f"  [{i+1}] {doc_id} - Table {table_num} | ID: {table_id}")
                log_message(f"      Title: {table_title[:80]}")
                log_message(f"      Content: {content_preview}...")
            else:
                section = node.metadata.get('section_id', 'N/A')
                log_message(f"  [{i+1}] {doc_id} - Text section {section}")
        
        log_message(f"UNIQUE NODES: {len(unique_retrieved)} nodes")
        
        # Simple reranking with NORMALIZED question and PARAMETERIZED top_k
        reranked_nodes = rerank_nodes(enhanced_question, unique_retrieved, reranker, 
                                     top_k=rerank_top_k)  # NOW PARAMETERIZED
        
        # Direct query without formatting - use normalized question
        response = query_engine.query(enhanced_question)
        
        end_time = time.time()
        processing_time = end_time - start_time
        
        log_message(f"Обработка завершена за {processing_time:.2f}с")
        
        sources_html = generate_sources_html(reranked_nodes, chunks_df)
        
        answer_with_time = f"""<div style='background-color: #2d3748; color: white; padding: 20px; border-radius: 10px; margin-bottom: 10px;'>
        <h3 style='color: #63b3ed; margin-top: 0;'>Ответ (Модель: {current_model}):</h3>
        <div style='line-height: 1.6; font-size: 16px;'>{response.response}</div>
        <div style='margin-top: 15px; padding-top: 10px; border-top: 1px solid #4a5568; font-size: 14px; color: #a0aec0;'>
        Время обработки: {processing_time:.2f} секунд 
        </div>
        </div>"""
        log_message(f"Model Answer: {response.response}")
        
        chunk_info = []
        for node in reranked_nodes:
            metadata = node.metadata if hasattr(node, 'metadata') else {}
            chunk_info.append({
                'document_id': metadata.get('document_id', 'unknown'),
                'section_id': metadata.get('section_id', 'unknown'),
                'section_path': metadata.get('section_path', ''),
                'section_text': metadata.get('section_text', ''),
                'type': metadata.get('type', 'text'),
                'table_number': metadata.get('table_number', ''),
                'image_number': metadata.get('image_number', ''),
                'chunk_size': len(node.text),
                'chunk_text': node.text
            })
        from app import create_chunks_display_html
        chunks_html = create_chunks_display_html(chunk_info)

        return answer_with_time, sources_html, chunks_html
        
    except Exception as e:
        log_message(f"Ошибка: {str(e)}")
        error_msg = f"<div style='background-color: #e53e3e; color: white; padding: 20px; border-radius: 10px;'>Ошибка: {str(e)}</div>"
        return error_msg, "", ""