chunks html added

utils.py

@@ -1,194 +1,206 @@
-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 format_context_for_llm(nodes):
-    context_parts = []
-    
-    for node in nodes:
-        metadata = node.metadata if hasattr(node, 'metadata') else {}
-        doc_id = metadata.get('document_id', 'Неизвестный документ')
-        
-        section_info = ""
-        
-        if metadata.get('section_path'):
-            section_path = metadata['section_path']
-            section_text = metadata.get('section_text', '')
-            parent_section = metadata.get('parent_section', '')
-            parent_title = metadata.get('parent_title', '')
-            
-            if metadata.get('level') in ['subsection', 'sub_subsection', 'sub_sub_subsection'] and parent_section and parent_title:
-                section_info = f"пункт {section_path} ({section_text}) в разделе {parent_section} ({parent_title})"
-            elif section_text:
-                section_info = f"пункт {section_path} ({section_text})"
-            else:
-                section_info = f"пункт {section_path}"
-        elif metadata.get('section_id'):
-            section_id = metadata['section_id']
-            section_text = metadata.get('section_text', '')
-            if section_text:
-                section_info = f"пункт {section_id} ({section_text})"
-            else:
-                section_info = f"пункт {section_id}"
-
-        if metadata.get('type') == 'table' and metadata.get('table_number'):
-            table_num = metadata['table_number']
-            if not str(table_num).startswith('№'):
-                table_num = f"№{table_num}"
-            section_info = f"таблица {table_num}"
-        
-        if metadata.get('type') == 'image' and metadata.get('image_number'):
-            image_num = metadata['image_number']
-            if not str(image_num).startswith('№'):
-                image_num = f"№{image_num}"
-            section_info = f"рисунок {image_num}"
-        
-        context_text = node.text if hasattr(node, 'text') else str(node)
-        
-        if section_info:
-            formatted_context = f"[ИСТОЧНИК: {section_info} документа {doc_id}]\n{context_text}\n"
-        else:
-            formatted_context = f"[ИСТОЧНИК: документ {doc_id}]\n{context_text}\n"
-        
-        context_parts.append(formatted_context)
-    
-    return "\n".join(context_parts)
-
-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>"
-    
-    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')
-        section_id = metadata.get('section_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>"
-            html += f"<h4 style='margin: 0 0 10px 0; color: #63b3ed;'>📌 {section_id}</h4>"
-
-        elif doc_type == 'table':
-            table_num = metadata.get('table_number', 'unknown')
-            if table_num and table_num != 'unknown':
-                if not 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>"
-            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')
-            section = metadata.get('section', '')
-            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} ({section})</h4>"
-            else:
-                html += f"<h4 style='margin: 0 0 10px 0; color: #fbb6ce;'>🖼️ Изображение - {doc_id} ({section})</h4>"
-        
-        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 answer_question(question, query_engine, reranker, current_model, chunks_df=None):
-    if query_engine is None:
-        return "<div style='background-color: #e53e3e; color: white; padding: 20px; border-radius: 10px;'>Система не инициализирована</div>", ""
-    
-    try:
-        log_message(f"Получен вопрос: {question}")
-        log_message(f"Используется модель: {current_model}")
-        start_time = time.time()
-        
-        log_message("Извлекаю релевантные узлы")
-        retrieved_nodes = query_engine.retriever.retrieve(question)
-        log_message(f"Извлечено {len(retrieved_nodes)} узлов")
-        for i in range(min(3, len(retrieved_nodes))):
-            log_message(f"Пример узла {i+1}: {retrieved_nodes[i].text[:200]}...")
-        
-        log_message("Применяю переранжировку")
-        reranked_nodes = rerank_nodes(question, retrieved_nodes, reranker, top_k=10)
-        
-        formatted_context = format_context_for_llm(reranked_nodes)
-        log_message(f"fорматированный контекст для LLM:\n{formatted_context[:500]}...")
-        
-        enhanced_question = f"""
-Контекст из базы данных:
-{formatted_context}
-
-Вопрос пользователя: {question}"""
-        
-        log_message(f"Отправляю запрос в LLM с {len(reranked_nodes)} узлами")
-        log_message(f"Вопрос для LLM:\n{enhanced_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} секунд")
-        
-        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>"""
-        
-        return answer_with_time, sources_html
-        
-    except Exception as e:
-        log_message(f"Ошибка обработки вопроса: {str(e)}")
-        error_msg = f"<div style='background-color: #e53e3e; color: white; padding: 20px; border-radius: 10px;'>Ошибка обработки вопроса: {str(e)}</div>"
+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 format_context_for_llm(nodes):
+    context_parts = []
+    
+    for node in nodes:
+        metadata = node.metadata if hasattr(node, 'metadata') else {}
+        doc_id = metadata.get('document_id', 'Неизвестный документ')
+        
+        section_info = ""
+        
+        if metadata.get('section_path'):
+            section_path = metadata['section_path']
+            section_text = metadata.get('section_text', '')
+            parent_section = metadata.get('parent_section', '')
+            parent_title = metadata.get('parent_title', '')
+            
+            if metadata.get('level') in ['subsection', 'sub_subsection', 'sub_sub_subsection'] and parent_section and parent_title:
+                section_info = f"пункт {section_path} ({section_text}) в разделе {parent_section} ({parent_title})"
+            elif section_text:
+                section_info = f"пункт {section_path} ({section_text})"
+            else:
+                section_info = f"пункт {section_path}"
+        elif metadata.get('section_id'):
+            section_id = metadata['section_id']
+            section_text = metadata.get('section_text', '')
+            if section_text:
+                section_info = f"пункт {section_id} ({section_text})"
+            else:
+                section_info = f"пункт {section_id}"
+
+        if metadata.get('type') == 'table' and metadata.get('table_number'):
+            table_num = metadata['table_number']
+            if not str(table_num).startswith('№'):
+                table_num = f"№{table_num}"
+            section_info = f"таблица {table_num}"
+        
+        if metadata.get('type') == 'image' and metadata.get('image_number'):
+            image_num = metadata['image_number']
+            if not str(image_num).startswith('№'):
+                image_num = f"№{image_num}"
+            section_info = f"рисунок {image_num}"
+        
+        context_text = node.text if hasattr(node, 'text') else str(node)
+        
+        if section_info:
+            formatted_context = f"[ИСТОЧНИК: {section_info} документа {doc_id}]\n{context_text}\n"
+        else:
+            formatted_context = f"[ИСТОЧНИК: документ {doc_id}]\n{context_text}\n"
+        
+        context_parts.append(formatted_context)
+    
+    return "\n".join(context_parts)
+
+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>"
+    
+    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')
+        section_id = metadata.get('section_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>"
+            html += f"<h4 style='margin: 0 0 10px 0; color: #63b3ed;'>📌 {section_id}</h4>"
+
+        elif doc_type == 'table':
+            table_num = metadata.get('table_number', 'unknown')
+            if table_num and table_num != 'unknown':
+                if not 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>"
+            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')
+            section = metadata.get('section', '')
+            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} ({section})</h4>"
+            else:
+                html += f"<h4 style='margin: 0 0 10px 0; color: #fbb6ce;'>🖼️ Изображение - {doc_id} ({section})</h4>"
+        
+        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 answer_question(question, query_engine, reranker, current_model, chunks_df=None):
+    if query_engine is None:
+        return "<div style='background-color: #e53e3e; color: white; padding: 20px; border-radius: 10px;'>Система не инициализирована</div>", ""
+    
+    try:
+        log_message(f"Получен вопрос: {question}")
+        log_message(f"Используется модель: {current_model}")
+        start_time = time.time()
+        
+        log_message("Извлекаю релевантные узлы")
+        retrieved_nodes = query_engine.retriever.retrieve(question)
+        log_message(f"Извлечено {len(retrieved_nodes)} узлов")
+        for i in range(min(3, len(retrieved_nodes))):
+            log_message(f"Пример узла {i+1}: {retrieved_nodes[i].text[:200]}...")
+        
+        log_message("Применяю переранжировку")
+        reranked_nodes = rerank_nodes(question, retrieved_nodes, reranker, top_k=10)
+        
+        formatted_context = format_context_for_llm(reranked_nodes)
+        log_message(f"fорматированный контекст для LLM:\n{formatted_context[:500]}...")
+        
+        enhanced_question = f"""
+Контекст из базы данных:
+{formatted_context}
+
+Вопрос пользователя: {question}"""
+        
+        log_message(f"Отправляю запрос в LLM с {len(reranked_nodes)} узлами")
+        log_message(f"Вопрос для LLM:\n{enhanced_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} секунд")
+        
+        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>"""
+        
+        chunk_info = []
+        for node in reranked_nodes:
+            section_id = node.metadata.get('section_id', node.metadata.get('section', 'unknown'))
+            chunk_info.append({
+                'document_id': node.metadata.get('document_id', 'unknown'),
+                'section_id': section_id,
+                '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 обработки вопроса: {str(e)}")
+        error_msg = f"<div style='background-color: #e53e3e; color: white; padding: 20px; border-radius: 10px;'>Ошибка обработки вопроса: {str(e)}</div>"
         return error_msg, ""