236 lines
10 KiB
Python
236 lines
10 KiB
Python
from django.shortcuts import render
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from elasticsearch_dsl import Search, Q
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from elasticsearch_dsl.connections import connections
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from django.conf import settings
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import re
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from django.http import JsonResponse
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# Configuração da conexão com o Elasticsearch
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connections.create_connection(hosts=[settings.ELASTICSEARCH_HOSTS])
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def spellcheck_view(request):
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query = request.GET.get('q', '')
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suggestions = []
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if query:
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s = Search(index='pdf_documents')
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s = s.suggest('auto_correct', query,
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phrase={
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'field': 'suggest',
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'size': 3,
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'gram_size': 3,
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'confidence': 2.0,
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'direct_generator': [{
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'field': 'suggest',
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'suggest_mode': 'popular'
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}]
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})
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response = s.execute()
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if hasattr(response.suggest, 'auto_correct'):
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for option in response.suggest.auto_correct[0].options:
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suggestions.append(option.text)
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return JsonResponse({'suggestions': suggestions})
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def search_view(request):
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query = request.GET.get('q', '') # Obtém o termo de pesquisa da URL
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page = int(request.GET.get('page', 1))
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results = []
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suggestions = []
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spelling_correction = None
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total_hits = 0
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per_page = 10
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if query:
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# Processamento especial para termos entre aspas
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exact_phrases = re.findall(r'"([^"]*)"', query)
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# Remove os termos entre aspas da consulta principal
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cleaned_query = query
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for phrase in exact_phrases:
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cleaned_query = cleaned_query.replace(f'"{phrase}"', '')
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# Remove espaços extras e pontuação desnecessária
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cleaned_query = re.sub(r'\s+', ' ', cleaned_query).strip()
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# Cria uma consulta no Elasticsearch
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search = Search(index='pdf_documents')
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# Lista para armazenar todas as consultas
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queries = []
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# Adiciona consulta para termos gerais (com fuzziness para tolerância a erros)
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if cleaned_query:
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queries.append(
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Q('multi_match',
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query=cleaned_query,
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fields=['title^3', 'content^2', 'synonyms^1'],
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fuzziness='AUTO',
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boost=2,)
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)
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queries.append(
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Q('match',
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synonyms={
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'query': cleaned_query,
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'boost': 0.5
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})
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)
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# Adiciona consultas exatas para frases entre aspas (sem fuzziness)
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for phrase in exact_phrases:
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if phrase.strip():
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# Consulta de frase exata para o título com peso alto
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queries.append(
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Q('match_phrase',
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title={
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'query': phrase,
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'boost': 3,
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'slop': 0 # Sem flexibilidade na ordem das palavras
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})
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)
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# Consulta de frase exata para o conteúdo com peso médio
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queries.append(
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Q('match_phrase',
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content={
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'query': phrase,
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'boost': 2,
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'slop': 0 # Sem flexibilidade na ordem das palavras
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})
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)
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# Combina as consultas com OR (se houver alguma)
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if queries:
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search = search.query(
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Q('bool', should=queries, minimum_should_match=1)
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)
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# Configuração do highlight para mostrar mais contexto
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search = search.highlight('content', fragment_size=300, number_of_fragments=2, pre_tags=['<mark>'], post_tags=['</mark>'])
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search = search.highlight('title', fragment_size=300, number_of_fragments=1, pre_tags=['<mark>'], post_tags=['</mark>'])
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# Paginação
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search = search[(page-1)*per_page:page*per_page]
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# Executa a consulta
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response = search.execute()
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total_hits = response.hits.total.value
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# Processa os resultados
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for hit in response:
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# Extrai o conteúdo destacado ou usa o original
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if hasattr(hit.meta, 'highlight') and hasattr(hit.meta.highlight, 'content'):
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highlighted_content = ' ... '.join(hit.meta.highlight.content)
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else:
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# Se não houver highlight, pegue os primeiros 300 caracteres
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highlighted_content = hit.content[:300] + '...' if len(hit.content) > 300 else hit.content
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# Extrai o título destacado ou usa o original
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if hasattr(hit.meta, 'highlight') and hasattr(hit.meta.highlight, 'title'):
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highlighted_title = hit.meta.highlight.title[0]
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else:
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highlighted_title = hit.title
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# Verifica se o resultado corresponde a uma frase exata
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is_exact_match = any(phrase.lower() in hit.content.lower() or
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phrase.lower() in hit.title.lower()
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for phrase in exact_phrases)
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results.append({
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'id': hit.meta.id,
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'title': hit.title,
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'highlighted_title': highlighted_title,
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'highlighted_content': highlighted_content,
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'uploaded_at': hit.uploaded_at,
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'score': hit.meta.score,
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'is_exact_match': is_exact_match
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})
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# Sugestões "Você quis dizer" (apenas para termos fora de aspas)
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if total_hits < 5 and cleaned_query:
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suggestion_search = Search(index='pdf_documents')
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suggestion_search = suggestion_search.suggest(
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'term_suggestion',
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cleaned_query,
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term={
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'field': 'content',
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'suggest_mode': 'popular',
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'size': 5
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}
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)
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suggestion_response = suggestion_search.execute()
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if hasattr(suggestion_response, 'suggest') and hasattr(suggestion_response.suggest, 'term_suggestion'):
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for suggestion in suggestion_response.suggest.term_suggestion:
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for option in suggestion.options:
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suggestions.append(option.text)
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# Cria uma correção ortográfica se necessário
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if suggestions and total_hits == 0:
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corrected_query = cleaned_query
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for suggestion_term in suggestion_response.suggest.term_suggestion:
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if suggestion_term.options:
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# Substitui palavras incorretas por sugestões
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word_to_replace = suggestion_term.text
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corrected_word = suggestion_term.options[0].text
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corrected_query = re.sub(r'\b' + re.escape(word_to_replace) + r'\b',
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corrected_word,
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corrected_query,
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flags=re.IGNORECASE)
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# Reconstrói a consulta original mantendo as frases entre aspas
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if corrected_query != cleaned_query:
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spelling_correction = corrected_query
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for phrase in exact_phrases:
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spelling_correction += f' "{phrase}"'
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spelling_correction = spelling_correction.strip()
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# Busca por termos relacionados (apenas se houver poucos resultados)
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if total_hits < 3 and cleaned_query:
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related_terms = Search(index='pdf_documents')
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related_terms = related_terms.query(
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'more_like_this',
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fields=['content', 'title'],
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like=cleaned_query,
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min_term_freq=1,
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max_query_terms=10,
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min_doc_freq=1
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)
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related_terms = related_terms[:5]
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related_response = related_terms.execute()
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for hit in related_response:
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# Verifica se este documento já está nos resultados
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if not any(r.get('id') == hit.meta.id for r in results):
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results.append({
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'id': hit.meta.id,
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'title': hit.title,
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'highlighted_title': hit.title,
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'highlighted_content': hit.content[:300] + '...' if len(hit.content) > 300 else hit.content,
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'uploaded_at': hit.uploaded_at,
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'score': hit.meta.score,
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'is_related': True
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})
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# Calcula a paginação
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total_pages = (total_hits + per_page - 1) // per_page if total_hits > 0 else 0
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# Renderiza o template com os resultados
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return render(request, 'diarios/search_results.html', {
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'query': query,
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'results': results,
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'suggestions': suggestions[:5], # Limita a 5 sugestões
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'spelling_correction': spelling_correction,
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'total_hits': total_hits,
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'page': page,
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'total_pages': total_pages,
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'page_range': range(max(1, page-2), min(total_pages+1, page+3)),
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'has_exact_phrases': bool(exact_phrases)
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})
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