+ author_results = srch.search_phrase(toks, 'authors', fuzzy=fuzzy, tokens_cache=tokens_cache)
+ title_results = srch.search_phrase(toks, 'title', fuzzy=fuzzy, tokens_cache=tokens_cache)
+
+ # Boost main author/title results with mixed search, and save some of its results for end of list.
+ # boost author, title results
+ author_title_mixed = srch.search_some(toks, ['authors', 'title', 'tags'], fuzzy=fuzzy, tokens_cache=tokens_cache)
+ author_title_rest = []
+ for b in author_title_mixed:
+ bks = filter(lambda ba: ba.book_id == b.book_id, author_results + title_results)
+ for b2 in bks:
+ b2.boost *= 1.1
+ if bks is []:
+ author_title_rest.append(b)
+
+ # Do a phrase search but a term search as well - this can give us better snippets then search_everywhere,
+ # Because the query is using only one field.
+ text_phrase = SearchResult.aggregate(
+ srch.search_phrase(toks, 'content', fuzzy=fuzzy, tokens_cache=tokens_cache, snippets=True, book=False, slop=4),
+ srch.search_some(toks, ['content'], tokens_cache=tokens_cache, snippets=True, book=False))
+
+ everywhere = srch.search_everywhere(toks, fuzzy=fuzzy, tokens_cache=tokens_cache)
+
+ def already_found(results):
+ def f(e):
+ for r in results:
+ if e.book_id == r.book_id:
+ e.boost = 0.9
+ results.append(e)
+ return True
+ return False
+ return f
+ f = already_found(author_results + title_results + text_phrase)
+ everywhere = filter(lambda x: not f(x), everywhere)
+
+ author_results = SearchResult.aggregate(author_results)
+ title_results = SearchResult.aggregate(title_results)
+
+ everywhere = SearchResult.aggregate(everywhere, author_title_rest)
+
+ for res in [author_results, title_results, text_phrase, everywhere]:
+ res.sort(reverse=True)
+ for r in res:
+ for h in r.hits:
+ h['snippets'] = map(lambda s:
+ re.subn(r"(^[ \t\n]+|[ \t\n]+$)", u"",
+ re.subn(r"[ \t\n]*\n[ \t\n]*", u"\n", s)[0])[0], h['snippets'])
+
+ suggestion = did_you_mean(query, srch.get_tokens(toks, field="SIMPLE"))
+ print "dym? %s" % repr(suggestion).encode('utf-8')