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aumento de velocidad y cambios en el tema de noticias relacionadas

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jlimolina 2026-01-25 07:33:57 +01:00
parent 3eca832c1a
commit 95adc07f37
9 changed files with 275 additions and 97 deletions
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21
inspect_qdrant.py Normal file
View file

@ -0,0 +1,21 @@
import os
import sys
sys.path.append(os.getcwd())
from utils.qdrant_search import get_qdrant_client
client = get_qdrant_client()
collection_name = "news_vectors"
# Scroll some points to see payload
response = client.scroll(
collection_name=collection_name,
limit=5,
with_payload=True,
with_vectors=False
)
for point in response[0]:
print(f"ID: {point.id}")
print(f"Payload: {point.payload}")
print("-" * 20)

155
models/noticias.py
View file

@ -1,7 +1,7 @@
from psycopg2 import extras
from typing import List, Dict, Optional, Tuple, Any
import os
# from sentence_transformers import SentenceTransformer (Moved to functions to avoid heavy start-up)
from typing import List, Dict, Optional, Tuple, Any
from psycopg2 import extras
from utils.qdrant_search import semantic_search
def _extraer_tags_por_traduccion(cur, traduccion_ids: List[int]) -> Dict[int, List[tuple]]:
@ -105,8 +105,24 @@ def buscar_noticias(
cur.execute("SELECT reltuples::bigint FROM pg_class WHERE relname = 'noticias'")
row = cur.fetchone()
total_results = row[0] if row else 0
elif q and not (categoria_id or pais_id or continente_id or fecha):
# Conteo optimizado para búsqueda simple (UNION de hits en noticias y traducciones)
cur.execute(
"""
SELECT COUNT(DISTINCT id) FROM (
SELECT id FROM noticias
WHERE search_vector_es @@ websearch_to_tsquery('spanish', %s)
UNION ALL
SELECT noticia_id as id FROM traducciones
WHERE search_vector_es @@ websearch_to_tsquery('spanish', %s)
AND lang_to = %s AND status = 'done'
) as all_hits
""",
(q, q, lang),
)
total_results = cur.fetchone()[0]
else:
# Conteo exacto si hay filtros (necesario para paginación filtrada)
# Conteo exacto si hay filtros combinados
cur.execute(
f"""
SELECT COUNT(n.id)
@ -175,16 +191,7 @@ def buscar_noticias(
return noticias, total_results, total_pages, tags_por_tr
# Cache del modelo para no cargarlo en cada petición
_model_cache = {}
def _get_emb_model():
from sentence_transformers import SentenceTransformer
model_name = os.environ.get("EMB_MODEL", "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
if model_name not in _model_cache:
device = "cuda" if torch.cuda.is_available() else "cpu"
_model_cache[model_name] = SentenceTransformer(model_name, device=device)
return _model_cache[model_name], model_name
# Embedding model loading moved to utils.qdrant_search
def buscar_noticias_semantica(
conn,
@ -194,77 +201,89 @@ def buscar_noticias_semantica(
categoria_id: Optional[str] = None,
continente_id: Optional[str] = None,
pais_id: Optional[str] = None,
fecha: Optional[str] = None,
fecha: Optional[Any] = None,
lang: str = "es",
) -> Tuple[List[Dict], int, int, Dict]:
"""
Búsqueda semántica usando embeddings y similitud coseno (vía producto punto si están normalizados).
Búsqueda semántica optimizada usando Qdrant.
Cae de vuelta a búsqueda tradicional si falla.
"""
if not q.strip():
return buscar_noticias(conn, page, per_page, "", categoria_id, continente_id, pais_id, fecha, lang)
offset = (page - 1) * per_page
model, model_name = _get_emb_model()
# Generar embedding de la consulta
q_emb = model.encode([q], normalize_embeddings=True)[0].tolist()
where = ["t.status = 'done'", "t.lang_to = %s"]
params = [lang]
if fecha:
where.append("n.fecha::date = %s")
params.append(fecha)
# Preparar filtros para Qdrant
q_filters = {"lang": lang}
if categoria_id:
where.append("n.categoria_id = %s")
params.append(int(categoria_id))
q_filters["categoria_id"] = int(categoria_id)
if pais_id:
where.append("n.pais_id = %s")
params.append(int(pais_id))
elif continente_id:
where.append("p.continente_id = %s")
params.append(int(continente_id))
q_filters["pais_id"] = int(pais_id)
# Nota: No filtramos por fecha o continente en Qdrant por ahora para simplicidad,
# ya que requeriría lógica más compleja de filtrado en Qdrant (rango o joins manuales).
# Realizar búsqueda en Qdrant
# Obtenemos más resultados de los necesarios para permitir re-filtrado o mejor ranking
# Pero no demasiados para mantener la velocidad
limit_qdrant = min(page * per_page * 2, 500)
try:
results_q = semantic_search(
query=q,
limit=limit_qdrant,
score_threshold=0.35,
filters=q_filters
)
except Exception as e:
print(f"⚠️ Error en búsqueda Qdrant, usando fallback: {e}")
return buscar_noticias(conn, page, per_page, q, categoria_id, continente_id, pais_id, fecha, lang)
where_sql = " AND ".join(where)
if not results_q:
# Fallback a búsqueda tradicional si no hay resultados semánticos
return buscar_noticias(conn, page, per_page, q, categoria_id, continente_id, pais_id, fecha, lang)
# El total real en Qdrant para esta búsqueda es difícil de saber sin una query de conteo separada,
# estimamos o usamos el tamaño de la lista retornada (limitada por nuestro umbral).
total_results = len(results_q)
total_pages = (total_results // per_page) + (1 if total_results % per_page else 0)
# Paginación sobre los resultados de Qdrant
offset = (page - 1) * per_page
paged_results_q = results_q[offset : offset + per_page]
if not paged_results_q:
return [], total_results, total_pages, {}
# Enriquecer resultados con datos frescos de PostgreSQL
news_ids = [r['news_id'] for r in paged_results_q]
with conn.cursor(cursor_factory=extras.DictCursor) as cur:
# Consulta de búsqueda vectorial (usamos un array_agg o similar para el producto punto si no hay pgvector)
# Nota: Aquí asumo que usamos producto punto entre arrays de double precision
query_sql = f"""
WITH similarity AS (
SELECT
te.traduccion_id,
(
SELECT SUM(a*b)
FROM unnest(te.embedding, %s::double precision[]) AS t(a,b)
) AS score
FROM traduccion_embeddings te
WHERE te.model = %s
)
SELECT
cur.execute(
"""
SELECT
n.id, n.titulo, n.resumen, n.url, n.fecha, n.imagen_url, n.fuente_nombre,
c.nombre AS categoria, p.nombre AS pais,
t.id AS traduccion_id, t.titulo_trad AS titulo_traducido, t.resumen_trad AS resumen_traducido,
TRUE AS tiene_traduccion, s.score
FROM similarity s
JOIN traducciones t ON t.id = s.traduccion_id
JOIN noticias n ON n.id = t.noticia_id
TRUE AS tiene_traduccion
FROM noticias n
LEFT JOIN categorias c ON c.id = n.categoria_id
LEFT JOIN paises p ON p.id = n.pais_id
WHERE {where_sql}
ORDER BY n.fecha DESC NULLS LAST, s.score DESC
LIMIT %s OFFSET %s
"""
LEFT JOIN traducciones t ON t.noticia_id = n.id AND t.lang_to = %s AND t.status = 'done'
WHERE n.id = ANY(%s)
""",
(lang, news_ids),
)
db_rows = {row['id']: row for row in cur.fetchall()}
# Para el conteo total en semántica podemos simplificar o usar el mismo WHERE
cur.execute(f"SELECT COUNT(*) FROM traducciones t JOIN noticias n ON n.id = t.noticia_id LEFT JOIN paises p ON p.id = n.pais_id WHERE {where_sql}", params)
total_results = cur.fetchone()[0]
total_pages = (total_results // per_page) + (1 if total_results % per_page else 0)
cur.execute(query_sql, [q_emb, model_name] + params + [per_page, offset])
noticias = cur.fetchall()
tr_ids = [n["traduccion_id"] for n in noticias]
# Mantener el orden de relevancia de Qdrant
noticias_enriquecidas = []
for r_q in paged_results_q:
nid = r_q['news_id']
if nid in db_rows:
row = dict(db_rows[nid])
row['score'] = r_q['score'] # Añadir score de relevancia
noticias_enriquecidas.append(row)
# Tags
tr_ids = [n["traduccion_id"] for n in noticias_enriquecidas if n.get("traduccion_id")]
tags_por_tr = _extraer_tags_por_traduccion(cur, tr_ids)
return noticias, total_results, total_pages, tags_por_tr
return noticias_enriquecidas, total_results, total_pages, tags_por_tr

8
routers/noticia.py
View file

@ -93,17 +93,21 @@ def noticia():
cur.execute(
"""
SELECT
n2.id,
n2.url,
n2.titulo,
n2.titulo AS titulo_original,
n2.fecha,
n2.imagen_url,
n2.fuente_nombre,
rn.score,
t2.titulo_trad,
t2.id AS related_tr_id
t2.id AS traduccion_id,
c.nombre AS categoria,
TRUE AS tiene_traduccion
FROM related_noticias rn
JOIN traducciones t2 ON t2.id = rn.related_traduccion_id
JOIN noticias n2 ON n2.id = t2.noticia_id
LEFT JOIN categorias c ON c.id = n2.categoria_id
WHERE rn.traduccion_id = %s
ORDER BY rn.score DESC
LIMIT 8;

3
routers/search.py
View file

@ -42,7 +42,8 @@ def search():
semantic_results = semantic_search(
query=q,
limit=max_qdrant_results,
score_threshold=0.3 # Umbral más bajo para capturar más resultados
score_threshold=0.3, # Umbral más bajo para capturar más resultados
filters={"lang": lang}
)
if semantic_results:

1
routers/stats.py
View file

@ -103,6 +103,7 @@ def aggregate_normalized_entities(rows, entity_type='persona'):
@stats_bp.route("/")
@cached(ttl_seconds=600, prefix="stats_index")
def index():
"""Stats dashboard page."""

165
templates/noticia_classic.html
View file

@ -127,6 +127,41 @@
{% endif %}
</div>
</div>
<!-- Sección de Artículos Relacionados debajo del contenido -->
{% if related_news %}
<section class="related-section">
<h3 class="section-title">Artículos Relacionados</h3>
<div class="related-grid">
{% for related in related_news %}
<div class="related-card">
{% if related.imagen_url %}
<div class="related-card-image">
<img src="{{ related.imagen_url }}" alt="{{ related.titulo_trad or related.titulo_original }}">
</div>
{% endif %}
<div class="related-card-body">
<span class="related-badge">{{ related.categoria or 'General' }}</span>
<h4>
{% if related.traduccion_id %}
<a href="{{ url_for('noticia.noticia', tr_id=related.traduccion_id) }}">
{% else %}
<a href="{{ url_for('noticia.noticia', id=related.id) }}">
{% endif %}
{{ related.titulo_trad or related.titulo_original }}
</a>
</h4>
<div class="related-footer">
<span><i class="fas fa-newspaper"></i> {{ related.fuente_nombre }}</span>
<span><i class="far fa-clock"></i> {{ related.fecha.strftime('%d/%m %H:%M') if related.fecha else ''
}}</span>
</div>
</div>
</div>
{% endfor %}
</div>
</section>
{% endif %}
</div>
<!-- Sidebar del artículo -->
@ -150,29 +185,6 @@
</div>
</div>
<!-- Artículos relacionados -->
{% if related_news %}
<div class="card">
<h3>Artículos Relacionados</h3>
<ul>
{% for related in related_news[:5] %}
<li>
{% if related.traduccion_id %}
<a href="{{ url_for('noticia.noticia', tr_id=related.traduccion_id) }}">
{% else %}
<a href="{{ url_for('noticia.noticia', id=related.id) }}">
{% endif %}
{{ related.titulo_trad if related.tiene_traduccion else related.titulo_original }}
</a>
<small style="color: var(--muted-color);">
{{ related.fecha.strftime('%d/%m %H:%M') if related.fecha else '' }}
</small>
</li>
{% endfor %}
</ul>
</div>
{% endif %}
<!-- Categorías populares -->
{% if categorias %}
<div class="card">
@ -339,6 +351,113 @@
margin-top: 5px;
font-size: 0.9rem;
}
.related-section {
margin-top: 50px;
padding-top: 30px;
border-top: 1px solid var(--border-color);
}
.section-title {
font-size: 1.5rem;
color: var(--text-color);
margin-bottom: 25px;
position: relative;
padding-bottom: 10px;
}
.section-title::after {
content: '';
position: absolute;
bottom: 0;
left: 0;
width: 60px;
height: 3px;
background: var(--accent-color);
}
.related-grid {
display: grid;
grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
gap: 20px;
margin-bottom: 40px;
}
.related-card {
background: var(--paper-color);
border: 1px solid var(--border-color);
border-radius: 8px;
overflow: hidden;
transition: transform 0.2s, box-shadow 0.2s;
display: flex;
flex-direction: column;
}
.related-card:hover {
transform: translateY(-5px);
box-shadow: 0 10px 20px rgba(0,0,0,0.1);
}
.related-card-image {
height: 160px;
overflow: hidden;
}
.related-card-image img {
width: 100%;
height: 100%;
object-fit: cover;
transition: transform 0.3s;
}
.related-card:hover .related-card-image img {
transform: scale(1.05);
}
.related-card-body {
padding: 15px;
flex-grow: 1;
display: flex;
flex-direction: column;
}
.related-badge {
font-size: 0.7rem;
text-transform: uppercase;
color: var(--accent-color);
font-weight: bold;
margin-bottom: 8px;
}
.related-card h4 {
font-size: 1rem;
line-height: 1.4;
margin-bottom: 15px;
display: -webkit-box;
-webkit-line-clamp: 3;
-webkit-box-orient: vertical;
overflow: hidden;
flex-grow: 1;
}
.related-card h4 a {
color: var(--text-color);
text-decoration: none;
}
.related-card h4 a:hover {
color: var(--accent-color);
}
.related-footer {
display: flex;
justify-content: space-between;
font-size: 0.75rem;
color: var(--muted-color);
margin-top: auto;
padding-top: 10px;
border-top: 1px solid var(--bg-color);
}
`;
document.head.appendChild(style);
</script>

15
utils/qdrant_search.py
View file

@ -13,6 +13,7 @@ QDRANT_HOST = os.environ.get("QDRANT_HOST", "localhost")
QDRANT_PORT = int(os.environ.get("QDRANT_PORT", "6333"))
QDRANT_COLLECTION = os.environ.get("QDRANT_COLLECTION_NAME", "news_vectors")
EMB_MODEL = os.environ.get("EMB_MODEL", "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
EMB_DEVICE = os.environ.get("EMB_DEVICE", "cpu") # Default to CPU, but check env
# Singleton para clientes globales
_qdrant_client: Optional[QdrantClient] = None
@ -47,7 +48,15 @@ def get_embedding_model() -> Any:
global _embedding_model
if _embedding_model is None:
from sentence_transformers import SentenceTransformer
_embedding_model = SentenceTransformer(EMB_MODEL, device='cpu')
import torch
device = EMB_DEVICE
if device == "cuda" and not torch.cuda.is_available():
print("⚠️ CUDA solicitado pero no disponible, usando CPU")
device = "cpu"
print(f"🤖 Cargando modelo de embeddings: {EMB_MODEL} en {device}")
_embedding_model = SentenceTransformer(EMB_MODEL, device=device)
return _embedding_model
@ -90,6 +99,10 @@ def semantic_search(
conditions = []
for key, value in filters.items():
if value is not None:
if key == "lang" and isinstance(value, str) and len(value) < 5:
# Character(5) in Postgres pads with spaces
value = value.ljust(5)
conditions.append(
FieldCondition(key=key, match=MatchValue(value=value))
)

2
workers/qdrant_worker.py
View file

@ -109,7 +109,7 @@ def get_pending_news(limit: int = BATCH_SIZE) -> List[Dict[str, Any]]:
SELECT
t.id as traduccion_id,
t.noticia_id,
t.lang_to as lang,
TRIM(t.lang_to) as lang,
t.titulo_trad as titulo,
t.resumen_trad as resumen,
n.url,

2
workers/translation_worker.py
View file

@ -304,7 +304,7 @@ def _translate_texts(src, tgt, texts, beams, max_new_tokens):
target_prefix=target_prefix,
beam_size=beams,
max_decoding_length=max_new,
repetition_penalty=1.1,
repetition_penalty=1.2,
no_repeat_ngram_size=4,
)
dt = time.time() - start