feat: Implement new sales data analysis agents and utilities

- Added Router_de_agentes.py to manage agent interactions for sales data analysis. - Created Analizador_de_datos_de_ventas.yaml for generating structured text reports from sales data. - Developed Generador_sql_ventas.yaml for generating SQL queries to analyze sales data. - Established Router_de_agente.yaml as a routing mechanism for agent requests. - Compiled centros_disponibles.md listing available sales centers. - Introduced primera_ejecucion_de_un_agente.py as an example for executing agents. - Added ver_los_prompts_de_un_agente.py to inspect prompts sent to OpenAI. - Included service account key for BigQuery access in rag-datasets-reader-sa-key.json. - Defined schema for sales data in Objeto_ventas.json. - Implemented utility functions for querying BigQuery in conseguir_datos_bq.py. - Created data transformation utilities in transformar_datos.py for handling decimal and date formats.
2025-10-06 18:48:19 +02:00
parent f1e456ea05
commit 28e50b921c
28 changed files with 1309 additions and 164 deletions
@@ -0,0 +1,378 @@
+# Constantes ##########################################################################
+
+PROMPT = "Dame los datos de ventas de los ultimos 3 meses del centro de Alcobendas"
+
+
+# Librerias de Agno ################################################################
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+
+# Prefect imports ##################################################################
+
+from prefect import task, flow
+from prefect.logging import get_run_logger
+from prefect.filesystems import LocalFileSystem
+
+local_file_system_block = LocalFileSystem.load("localfile")
+
+# Cargar variables de entorno ######################################################
+
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+
+# Imports adicionales #########################################################
+
+import traceback
+from uuid import uuid4
+
+# Importaciones de Archivos #########################################################
+
+from jinja2 import Template
+import yaml
+import json
+
+
+# Utils ###################################################################################
+
+from utils.conseguir_datos_bq import consultar_bigquery_paginado
+from utils.transformar_datos import limpiar_datos_para_json
+
+
+
+
+#######################################################
+# Agentes ##########################################
+
+
+with open("agents/Generador_sql_ventas.yaml", "r", encoding="utf-8") as f:
+    experto_sql_ventas = yaml.safe_load(f)
+    
+with open("agents/Analizador_de_datos_de_ventas.yaml", "r", encoding="utf-8") as f:
+    analizador_de_datos_de_ventas = yaml.safe_load(f)
+
+
+
+
+#########################################################
+# Datos extras ##########################
+
+with open("schemas_bbdd/Objeto_ventas.json", "r", encoding="utf-8") as f:
+    schema_json_ventas = json.load(f)
+
+with open("detalles_para_agentes/centros_disponibles.md", "r", encoding="utf-8") as f:
+    detalles_centros = f.read()
+
+#############################################################
+# Datos añadidos a los agentes ##########################
+
+schema_str_ventas = json.dumps(schema_json_ventas, indent=2, ensure_ascii=False)
+contexto_ventas = {
+    "esquema_ventas": schema_str_ventas,
+    "centros_disponibles": detalles_centros
+}
+
+contexto_ventas_analizador = {
+    "centros_disponibles": detalles_centros
+}
+
+# Datos para Generador de SQL 
+
+template_ventas = Template(experto_sql_ventas["system_message"])
+rendered_yaml_ventas_sql = template_ventas.render(esquema_ventas=contexto_ventas)
+experto_sql_ventas["system_message"] = rendered_yaml_ventas_sql
+
+
+# Datos para Analizador de datos de ventas
+
+template_ventas_analizador = Template(analizador_de_datos_de_ventas["system_message"])
+rendered_yaml_ventas_analisis = template_ventas_analizador.render(esquema_ventas=contexto_ventas_analizador)
+analizador_de_datos_de_ventas["system_message"] = rendered_yaml_ventas_analisis
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+###############################################################
+# Tareas ##########################################################
+
+
+
+# Generar SQL ventas ######################################
+
+@task(name="Convierte_prompt_a_sql", log_prints=True)
+def Convierte_prompt_a_sql(prompt_de_usuario: str):
+    prefect_logger = get_run_logger()
+
+    prefect_logger.debug("Creando el agente con OpenAI")
+
+    agente = Agent(
+        model=OpenAIChat(id="gpt-4o-mini", api_key=openai_api_key),
+        name=experto_sql_ventas["name"],
+        description=experto_sql_ventas["description"],
+        system_message=experto_sql_ventas["system_message"],
+        debug_mode=True,
+    )
+    
+    prefect_logger.debug("Agente creado correctamente")
+    prefect_logger.debug("Enviando el prompt al agente")    
+    
+    try:
+        resultado = agente.run(f"devuelve el sql para el siguiente requerimiento: {prompt_de_usuario}")
+        prefect_logger.debug("Prompt enviado correctamente")
+        
+        # Imprime la respuesta del agente en el log Prefect
+        prefect_logger.info("=== RESPUESTA DEL AGENTE ===")
+        prefect_logger.info(str(resultado.content.strip()))  # imprime texto generado
+        prefect_logger.info("============================")
+        
+        # Si debug_mode está activado, puedes imprimir los logs internos del agente
+        if hasattr(agente, "messages"):
+            prefect_logger.debug("=== LOG INTERNO DEL AGENTE ===")
+            for msg in agente.messages:
+                prefect_logger.debug(f"{msg.role}: {msg.content}")
+            prefect_logger.debug("===============================")
+
+        prefect_logger.debug("Ejecución completada correctamente ✅")
+        return resultado.content.strip()
+        
+    except Exception as e:
+        prefect_logger.error("Error al enviar el prompt al agente")
+        traceback_str = traceback.format_exc()
+        prefect_logger.error(f"Traceback: {traceback_str}")
+        raise e
+    
+    
+    
+    
+    
+    
+   
+   
+ 
+
+# Conseguir datos de ventas a partir del SQL generado ##############################
+
+@task(name="Consigue_datos_ventas", log_prints=True)
+def Consigue_datos_ventas(sql_generado_por_agente: str, num_pagina_deseada: int = 1):
+    prefect_logger = get_run_logger()
+    prefect_logger.info("Iniciando consulta de datos de ventas en BigQuery...")
+
+    try:
+        paginas = consultar_bigquery_paginado(sql_generado_por_agente)
+
+        total_paginas = 0
+        resultados_finales = []
+
+        for pagina in paginas:
+            total_paginas += 1
+            if total_paginas == num_pagina_deseada:
+                prefect_logger.info(f"✅ Página {num_pagina_deseada} obtenida con {len(pagina)} filas")
+                resultados_finales = pagina
+
+        if not resultados_finales:
+            prefect_logger.warning(f"⚠️ No se encontró la página {num_pagina_deseada}. Total de páginas disponibles: {total_paginas}")
+            return {
+                "pagina": num_pagina_deseada,
+                "total_paginas": total_paginas,
+                "datos": [],
+                "mensaje": f"No se encontró la página {num_pagina_deseada}"
+            }
+
+        # Mostrar ejemplo en logs
+        ejemplo = resultados_finales[:5]
+        # prefect_logger.info("Ejemplo de datos obtenidos:")
+        # for fila in ejemplo:
+        #     prefect_logger.info(str(fila))
+
+        prefect_logger.info(
+            f"Consulta completada ✅ Página devuelta: {num_pagina_deseada} / {total_paginas} "
+            f"con {len(resultados_finales)} filas"
+        )
+
+        datos_ventas = {
+            "pagina": num_pagina_deseada,
+            "total_paginas": total_paginas,
+            "filas_en_pagina": len(resultados_finales),
+            "datos": resultados_finales,
+            "ejemplo": ejemplo,
+            "descripcion": (
+                f"Página {num_pagina_deseada} de {total_paginas}. "
+                f"Contiene {len(resultados_finales)} filas. "
+                "Los datos son los resultados de la consulta SQL proporcionada."
+            )
+        }
+        
+        prefect_logger.info(datos_ventas)
+        
+        # 📦 Devolver información accesible al agente
+        return datos_ventas
+
+    except Exception as e:
+        prefect_logger.error("❌ Error durante la consulta en BigQuery")
+        traceback_str = traceback.format_exc()
+        prefect_logger.error(f"Traceback: {traceback_str}")
+        raise e
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+# Analizador de resultados ##########################################################
+@task(name="Analizador_de_resultados", log_prints=True)
+def Analizador_de_resultados(datos_ventas: dict, prompt_de_usuario: str):
+    prefect_logger = get_run_logger()
+    prefect_logger.info("Iniciando análisis de resultados de ventas...")
+
+    try:
+        agente = Agent(
+            model=OpenAIChat(id="gpt-4o-mini", api_key=openai_api_key),
+            name=analizador_de_datos_de_ventas["name"],
+            description=analizador_de_datos_de_ventas["description"],
+            system_message=analizador_de_datos_de_ventas["system_message"],
+            debug_mode=True,
+        )
+
+        prefect_logger.info("Agente Analizador_de_datos creado correctamente ✅")
+
+        pagina_actual = datos_ventas.get("pagina", 1)
+        total_paginas = datos_ventas.get("total_paginas", 1)
+        filas_en_pagina = datos_ventas.get("filas_en_pagina", 0)
+
+        # 🔧 Limpieza de Decimals antes del dump
+        datos_limpiados = limpiar_datos_para_json(datos_ventas.get("datos", []))
+        datos_json = json.dumps(datos_limpiados, indent=2, ensure_ascii=False)
+
+        prompt_agente = f"""
+Analiza los siguientes datos de ventas y responde al requerimiento del usuario.
+
+🧠 Prompt del usuario:
+{prompt_de_usuario}
+
+📄 Información de la página actual:
+- Página actual: {pagina_actual} / {total_paginas}
+- Filas en esta página: {filas_en_pagina}
+
+📊 Datos de esta página:
+{datos_json}
+
+"""
+
+        resultado = agente.run(prompt_agente)
+
+        prefect_logger.info("✅ Análisis completado correctamente")
+        prefect_logger.info("=== RESULTADO DEL ANÁLISIS ===")
+        prefect_logger.info(resultado.content.strip())
+        prefect_logger.info("===============================")
+
+        return {
+            "prompt_usuario": prompt_de_usuario,
+            "pagina_analizada": pagina_actual,
+            "total_paginas": total_paginas,
+            "filas_analizadas": filas_en_pagina,
+            "analisis": resultado.content.strip(),
+            "datos_analizados": datos_limpiados,
+        }
+
+    except Exception as e:
+        prefect_logger.error("❌ Error durante el análisis de resultados")
+        traceback_str = traceback.format_exc()
+        prefect_logger.error(f"Traceback: {traceback_str}")
+        raise e
+
+
+
+
+
+
+
+
+
+
+
+
+# FLUJO PRINCIPAL ##########################################################
+
+@flow(name="Agente_ventas", result_storage=local_file_system_block, log_prints=True)  # type: ignore
+def Agente_ventas(PROMPT, num_pagina: int = 1):
+    prefect_logger = get_run_logger()
+    prefect_logger.info("🚀 Iniciando flujo Agente_ventas...")
+
+    # 🧠 1. Generar SQL a partir del prompt
+    resultado_analisis = Convierte_prompt_a_sql.submit(PROMPT).result()
+
+    # 🧮 2. Intentar obtener los datos (máximo 3 intentos)
+    for intento in range(3):
+        try:
+            datos_ventas = Consigue_datos_ventas.submit(
+                resultado_analisis, num_pagina_deseada=num_pagina
+            ).result()
+            break  # ✅ Si la consulta fue exitosa, salir del bucle
+
+        except Exception as e:
+            # 🧰 En caso de error, regenerar el SQL con información del fallo
+            resultado_analisis = Convierte_prompt_a_sql.submit(f"""
+El SQL generado previamente daba error. El SQL era: {resultado_analisis}.
+El error fue: {str(e)}.
+Corrige el SQL para que no dé error y vuelve a generarlo.
+El prompt del usuario era: {PROMPT}
+""").result()
+
+            if intento == 2:
+                raise e  # ❌ Si falla 3 veces, detener el flujo
+
+    # 🧾 3. Logs de diagnóstico
+    print("=== SQL GENERADO ===")
+    print(resultado_analisis)
+    print("====================")
+    print("=== DATOS DE VENTAS OBTENIDOS ===")
+    print(datos_ventas)
+    print("==================================")
+
+    # 📊 4. Analizar los resultados con el agente analista
+    prefect_logger.info(f"🔎 Analizando los resultados de la página {num_pagina}...")
+
+    analisis = Analizador_de_resultados.submit(
+        datos_ventas, PROMPT
+    ).result()
+
+    # 🧠 5. Mostrar el análisis final
+    print(f"=== ANÁLISIS DE PÁGINA {num_pagina} ===")
+    print(analisis["analisis"])
+    print("===================================")
+
+    # ✅ 6. Devolver resultados combinados
+    return {
+        "sql_generado": resultado_analisis,
+        "analisis": analisis,
+    }
+
+
+# Ejecución directa del flujo
+if __name__ == "__main__":
+    # num_pagina=1 → analiza solo una página
+    Agente_ventas(PROMPT, num_pagina=1)
@@ -0,0 +1,44 @@
+# Constantes ##########################################################################
+
+PROMPT = "Dame la venta total del centro de velez malaga de marzo de 2023 desglosado por factura"
+
+
+# Librerias de Agno ################################################################
+
+from agno.agent import Agent
+from agno.models.openai import OpenAIChat
+
+# Prefect imports ##################################################################
+
+from prefect import task, flow
+from prefect.logging import get_run_logger
+from prefect.filesystems import LocalFileSystem
+
+local_file_system_block = LocalFileSystem.load("localfile")
+
+# Cargar variables de entorno ######################################################
+
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+openai_api_key = os.getenv("OPENAI_API_KEY")
+
+
+# Imports adicionales #########################################################
+
+import traceback
+from uuid import uuid4
+
+# Importaciones de Archivos #########################################################
+
+from jinja2 import Template
+import yaml
+import json
+
+
+# Utils ###################################################################################
+
+from utils.conseguir_datos_bq import consultar_bigquery_paginado
+from utils.transformar_datos import convertir_decimales
@@ -0,0 +1,32 @@
+name: Analizador de datos de ventas
+description: Agente que analiza los datos recibidos y genera un informe en texto claro y estructurado, sin generar código ni SQL.
+system_message: >
+  Eres un analista de datos de ventas con más de 200 años de experiencia combinada en análisis, interpretación y comunicación de información comercial.
+  Tu objetivo es examinar los datos proporcionados y redactar un informe narrativo que describa los patrones, tendencias y observaciones más relevantes.
+  
+  ➤ **Tu estilo**
+  - Redactas informes claros, concisos y bien estructurados.
+  - Usas un tono profesional y analítico, pero fácil de entender.
+  - No generas ni mencionas código, consultas SQL, funciones, ni instrucciones técnicas.
+  - No inventas datos ni haces suposiciones fuera del conjunto recibido.
+  
+  ➤ **Tu enfoque**
+  - Observa distribuciones, totales, promedios, máximos/mínimos, y comparaciones si son posibles con los datos entregados.
+  - Resume las diferencias entre centros, periodos o categorías si los datos lo permiten.
+  - Si detectas valores anómalos o inconsistencias, menciónalos brevemente.
+  - Evita hacer recomendaciones o predicciones: tu labor es descriptiva, no prescriptiva.
+
+  ➤ **Información de contexto**
+  Los centros disponibles para las ventas son:
+  {{centros_disponibles}}
+  (columna: "Centros___Centro_NavId__name")
+
+  ➤ **Tu salida esperada**
+  Genera **únicamente** un informe de texto (sin tablas ni gráficos), que:
+  - Resuma los hallazgos principales.
+  - Destaque las observaciones relevantes.
+  - Organice la información en secciones o párrafos temáticos.
+  - Sea comprensible para directivos o analistas sin conocimiento técnico.
+
+  ⚠️ **No generes SQL, código, fórmulas ni pseudocódigo.**
+  ⚠️ **Usa únicamente la información presente en los datos recibidos.**
@@ -0,0 +1,25 @@
+name: Consultor SQL de Ventas
+description: Agente que solamente tiene que generar una consulta SQL para analizar datos de ventas
+system_message: >
+  Eres un consultor SQL con 200 años de experiencia en análisis de datos de ventas.
+  Te especializas en extraer, transformar y cargar datos para generar informes
+  que ayuden a mejorar las estrategias de ventas
+  Agrupas efectivamente los datos y generas informes claros y concisos.
+  Recupera y analiza el dataset de ventas almacenado en la base de datos como autingo-159109.rag_datasets.Objeto_Ventas.
+  No hagas Joins, consigue todos los datos desdes la tabla "autingo-159109.rag_datasets.Objeto_Ventas"
+  No des explicaciones ni pasos intermedios, solo la consulta SQL.
+
+  Los centros disponibles para las ventas son:
+  {{centros_disponibles}} 
+  en la columna "Centros___Centro_NavId__name"
+
+  Si no se te especifica que uses un centro de Cristales o Glass no lo uses.
+  Utiliza su centro principal sin Cristales o Glass.
+
+  Utiliza SQL para BigQuery para extraer la información relevante y generar un informe inicial de los datos.
+  Utiliza este esquema para recuperar los datos de la tabla "autingo-159109.rag_datasets.Objeto_Ventas"
+  
+  COLUMNAS:
+  {{esquema_ventas}}
+
+  La salida esperada es una consulta SQL generada entre ```sql SELECT ...  ``` sin mas explicaciones.
@@ -0,0 +1,154 @@
+Avda. Toreros
+Fuengirola
+Alcala Henares CRISTALES
+San Fernando CRISTALES
+Leganes CRISTALES
+Alcobendas CRISTALES
+Las Rozas
+Alfafar
+Huelva
+MT Maria Auxiliadora CRISTALES
+Almeria
+Gta. Cadiz
+MT Cornella
+Pinto
+Parla
+Vaguada
+Islazul
+MT Vigo
+MT Pintor Sorolla - Colon
+MT Alexandre Rosello
+MT Diagonal
+Santa Engracia CRISTALES
+MT San Jose de Valderas CRISTALES
+Goya GLASS
+MT Avenida de Francia CRISTALES
+Vallecas
+MT Puerto Venecia
+MT Bahia de Santander
+MT Sanchinarro
+Cornella
+Vallecas CRISTALES
+Elche
+Villalba
+MT Avenida de España
+MT Gran Via
+Cordoba
+MT Princesa
+Majadahonda
+Store
+La Red
+Finestrat
+MT Castellana
+MT Nuevo Centro
+MT El Corte Ingles Cartagena
+MT Goya
+Aurgi Asociados
+Xativa
+Cornella 2
+Villalba CRISTALES
+Torrevieja
+Zaragoza
+La Maquinista
+MT Monasterio
+Puerto de Santa Maria
+MT San Juan de Aznalfarache
+MT Malaga
+MT Pozuelo
+MT Monasterio CRISTALES
+MT Girocentre
+Santa Engracia
+MT Nervion
+Linares
+Badajoz
+San Sebastian CRISTALES
+MT Gijon
+MT Avenida de la Libertad
+MT Talavera de la Reina
+Rivas
+Pola de Siero
+Mostoles
+MT Gran Casa
+MT Compostela CRISTALES
+Serrano
+MT Ramon y Cajal
+San Sebastian
+Goya
+MT Ronda de Cordoba
+Granada
+MT Paseo de Morella
+MT Ctra de Madrid-Irun km. 236 CRISTALES
+Hospitalet
+Alcorcon
+MT Costa de Marbella
+Puerto de Santa Maria CRISTALES
+MT Conquistadores
+Malaga
+Olias del Rey
+MT Ctra de Madrid-Irun km. 236
+Alcobendas
+MT Bahia de Cadiz
+MT Jerez
+Leganes
+SP EL ESCORIAL
+MT Avenida de Francia
+Mataro
+Torremolinos
+Gava
+MT Costa Mijas
+MT Compostela
+MT El Bercial
+MT Mendez Alvaro
+Vall D'Uixo
+Villanueva de la Serena
+Zaragoza CRISTALES
+MT Bahia de Malaga
+MT Ciudad de Elche
+AUR ALICANTE AV. NOVELDA
+MT Xanadu
+MT Web
+Les Franqueses
+Chamartin
+MT Alcala de Henares
+Aluche
+MT Cornella CRISTALES
+Las Rozas CRISTALES
+Sant Celoni
+MT Ramon y Cajal CRISTALES
+MT San Jose de Valderas
+San Fernando
+Tucan
+MT Campo de las Naciones
+Aurgi Web
+Barbera
+San Juan CRISTALES
+MT Pozuelo CRISTALES
+MT Siete Palmas
+Sabadell
+Roquetas
+Sant Adria
+MT Jaen
+Alcala Henares
+MT El Bercial CRISTALES
+Denia
+Unidad Movil Madrid Glass
+Aurgi Asociados Gruas
+Velez Malaga
+Jerez
+Arganda
+Emilio Muñoz
+MT Alicante
+Granollers
+Sant Boi
+MT Bahia de Algeciras
+Oficinas Centrales
+MT Maria Auxiliadora
+Barbera CRISTALES
+MT Tres de Mayo
+Autingo
+Sant Cugat
+SP JAVEA
+Store CRISTALES
+San Juan
+Marques Vadillo
+Valdemoro
@@ -19,12 +19,13 @@ load_dotenv()
 openai_api_key = os.getenv("OPENAI_API_KEY")


-prefect_logger = get_run_logger()
-
 # Definir tareas ###############################################

@task(name="Inicializar agente financiero", log_prints=True)
 def inicializar_agente_financiero():
+    prefect_logger = get_run_logger()
+    
+    
    prefect_logger.debug(f"Inicializando el agente financiero")
    # Aquí se podría agregar la lógica para inicializar el agente
    return "Agente financiero inicializado"
@@ -32,9 +33,9 @@ def inicializar_agente_financiero():

@task(name="Analizar datos financieros", log_prints=True)
 def analizar_datos_financieros(datos: str):
-
    prefect_logger = get_run_logger()
    
+    
    prefect_logger.debug(f"Creando el agente con OpenAI")

    prefect_logger.debug(f"analizando los datos: {datos}")
@@ -121,7 +121,7 @@ def averiguar_el_prompt_que_seenvia_a_openai(prompt_de_usuario: str):

 # Definir el flujo principal #########################################

-@flow(name="Flujo financiero", result_storage=local_file_system_block, log_prints=True)  # type: ignore
+@flow(name="Averiguar_prompt_enviado", result_storage=local_file_system_block, log_prints=True)  # type: ignore
 def flujo_principal():

    resultado_analisis = averiguar_el_prompt_que_seenvia_a_openai.submit(PROMPT).result()
@@ -1,64 +0,0 @@
-version: '3.9'
-services:
-  otel-collector:
-    image: otel/opentelemetry-collector-contrib:0.136.0
-    container_name: otel-collector
-    command:
-    - --config=/etc/otel/config.yaml
-    volumes:
-    - ./otel-config.yaml:/etc/otel/config.yaml
-    ports:
-    - 4317:4317
-    - 4318:4318
-    depends_on:
-    - victoria
-    - tempo
-  victoria:
-    image: victoriametrics/victoria-metrics:latest
-    container_name: victoria
-    ports:
-    - 8428:8428
-    volumes:
-    - ./victoria-data:/victoria-metrics-data
-    command:
-    - --storageDataPath=/victoria-metrics-data
-    - --retentionPeriod=3
-  tempo:
-    image: grafana/tempo:latest
-    container_name: tempo
-    ports:
-    - 3200:3200
-    volumes:
-    - ./tempo-data:/var/tempo
-    command:
-    - -config.file=/etc/tempo.yaml
-    configs:
-    - source: tempo_config
-      target: /etc/tempo.yaml
-  grafana-srv:
-    image: grafana/grafana-oss:latest
-    container_name: grafana-srv
-    ports:
-    - 33000:3000
-    environment:
-      GF_SECURITY_ADMIN_USER: admin
-      GF_SECURITY_ADMIN_PASSWORD: admin123
-      GF_USERS_ALLOW_SIGN_UP: 'false'
-    depends_on:
-    - victoria
-    - tempo
-    volumes:
-    - grafana_data:/var/lib/grafana
-    - ./provisioning/datasources:/etc/grafana/provisioning/datasources
-    - ./provisioning/dashboards:/etc/grafana/provisioning/dashboards
-    - ./grafana-dashboards:/var/lib/grafana/dashboards
-configs:
-  tempo_config:
-    content: "\nserver:\n  http_listen_port: 3200\n\ndistributor:\n  receivers:\n\
-      otlp:\n  protocols:\n    grpc:\n      endpoint: \"0.0.0.0:4317\"\n    http:\n\
-      \      endpoint: \"0.0.0.0:4318\"\n\ningester:\n  trace_idle_period: 10s\n \
-      \ max_block_bytes: 1000000\n  max_block_duration: 5m\n\ncompactor:\n  compaction:\n\
-      block_retention: 24h\n\nstorage:\n  trace:\nwal:\n  path: /var/tempo/wal\nlocal:\n\
-      \  path: /var/tempo/blocks\n"
-volumes:
-  grafana_data: {}
@@ -1,29 +0,0 @@
-{
-  "id": null,
-  "uid": "overview",
-  "title": "\ud83d\udcca System Overview",
-  "timezone": "browser",
-  "panels": [
-    {
-      "type": "graph",
-      "title": "CPU Usage",
-      "targets": [
-        {
-          "expr": "process_cpu_seconds_total",
-          "legendFormat": "{{instance}}"
-        }
-      ]
-    },
-    {
-      "type": "table",
-      "title": "Logs (simulados)",
-      "targets": [
-        {
-          "expr": "up"
-        }
-      ]
-    }
-  ],
-  "schemaVersion": 36,
-  "version": 1
-}
@@ -1,42 +0,0 @@
-receivers:
-  otlp:
-    protocols:
-      grpc: {}
-      http: {}
-processors:
-  batch: {}
-exporters:
-  prometheusremotewrite:
-    endpoint: http://victoria:8428/api/v1/write
-  otlp/tempo:
-    endpoint: http://tempo:4317
-    tls:
-      insecure: true
-  debug:
-    verbosity: normal
-service:
-  pipelines:
-    metrics:
-      receivers:
-      - otlp
-      processors:
-      - batch
-      exporters:
-      - prometheusremotewrite
-      - debug
-    traces:
-      receivers:
-      - otlp
-      processors:
-      - batch
-      exporters:
-      - otlp/tempo
-      - debug
-    logs:
-      receivers:
-      - otlp
-      processors:
-      - batch
-      exporters:
-      - prometheusremotewrite
-      - debug
@@ -1,10 +0,0 @@
-apiVersion: 1
-providers:
- name: default
-  orgId: 1
-  folder: ''
-  type: file
-  disableDeletion: false
-  updateIntervalSeconds: 10
-  options:
-    path: /var/lib/grafana/dashboards
@@ -1,11 +0,0 @@
-apiVersion: 1
-datasources:
- name: VictoriaMetrics
-  type: prometheus
-  access: proxy
-  url: http://victoria:8428
-  isDefault: true
- name: Tempo
-  type: tempo
-  access: proxy
-  url: http://tempo:3200
@@ -1 +0,0 @@
-田Jpu6�
@@ -1 +0,0 @@
-[]
@@ -1 +0,0 @@
-[]
@@ -1 +0,0 @@
-[]
@@ -6,6 +6,7 @@ readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
    "agno>=2.1.1",
+    "google-cloud-bigquery>=3.38.0",
    "icecream>=2.1.8",
    "marimo>=0.16.5",
    "openai>=2.1.0",
@@ -0,0 +1,13 @@
+{
+  "type": "service_account",
+  "project_id": "autingo-159109",
+  "private_key_id": "c43c626d47606344cd3a255d62df1be77019cc54",
+  "private_key": "-----BEGIN PRIVATE KEY-----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\nazDWb8s8eRHw+9nAS2TALg==\n-----END PRIVATE KEY-----\n",
+  "client_email": "rag-datasets-reader-sa@autingo-159109.iam.gserviceaccount.com",
+  "client_id": "111296269982496507213",
+  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
+  "token_uri": "https://oauth2.googleapis.com/token",
+  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
+  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/rag-datasets-reader-sa%40autingo-159109.iam.gserviceaccount.com",
+  "universe_domain": "googleapis.com"
+}
@@ -0,0 +1,359 @@
+[
+  {
+    "name": "Fecha",
+    "mode": "NULLABLE",
+    "type": "DATETIME",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "numeroDocumento",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "NumeroLineas",
+    "mode": "NULLABLE",
+    "type": "INTEGER",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "idCentro",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "idProducto",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "cantidad",
+    "mode": "NULLABLE",
+    "type": "BIGNUMERIC",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "precioUnitario",
+    "mode": "NULLABLE",
+    "type": "BIGNUMERIC",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Base_imponible_linea",
+    "mode": "NULLABLE",
+    "type": "BIGNUMERIC",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Tipo_de_documento",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "semana",
+    "mode": "NULLABLE",
+    "type": "INTEGER",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "ano",
+    "mode": "NULLABLE",
+    "type": "INTEGER",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "ano_mas_uno",
+    "mode": "NULLABLE",
+    "type": "INTEGER",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Fecha_mas_un_ano",
+    "mode": "NULLABLE",
+    "type": "DATETIME",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__nav_id",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__id_centro",
+    "mode": "NULLABLE",
+    "type": "INTEGER",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__name",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__Companies__name",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__Zones__name",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__Cluster_centros",
+    "mode": "NULLABLE",
+    "type": "INTEGER",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__Jefes_de_centro_y_Rmos___co_51ce9994",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__Jefes_de_centro_y_Rmos___co_c6afb8e8",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__Jefes_de_centro_y_Rmos___co_a5423963",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__Provincias_comunidades_y_su_b8d2c2a0",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__Provincias_comunidades_y_su_cb74d1a1",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Centros___Centro_NavId__Provincias_comunidades_y_su_9fd8d3fc",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__id",
+    "mode": "NULLABLE",
+    "type": "INTEGER",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__nav_id",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__description",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__is_tecdoc",
+    "mode": "NULLABLE",
+    "type": "BOOLEAN",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Status_de_producto",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Is_Encargo",
+    "mode": "NULLABLE",
+    "type": "BOOLEAN",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__is_service",
+    "mode": "NULLABLE",
+    "type": "BOOLEAN",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__is_tpv",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Tipo___Navision",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Product_Groups__description",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Product_Categories__description",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idAtributo1",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idAtributo2",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idAtributo3",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idAtributo5",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idAtributo4",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idAtributo6",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idAtributo7",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idAtributo8",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idAtributo9",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idAtributo10",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idCategoria",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Producto___Nav__idGrupo",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Categoria_16_07_cgq___t_cb30d712",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Categoria_16_07_cgq___t_d83f396f",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  },
+  {
+    "name": "Productos___Producto_NavId__Categoria_16_07_cgq___t_5e9550ff",
+    "mode": "NULLABLE",
+    "type": "STRING",
+    "description": "",
+    "fields": []
+  }
+]
@@ -0,0 +1,68 @@
+from google.cloud import bigquery
+from typing import Generator, List, Dict
+import os
+
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "rag-datasets-reader-sa-key.json"
+
+
+def consultar_bigquery_paginado(
+    sql_query: str,
+    page_size: int = 1000,
+    project_id: str | None = None
+) -> Generator[List[Dict], None, None]:
+    """
+    Ejecuta una consulta SQL de BigQuery y devuelve los resultados paginados.
+    Limpia el texto SQL automáticamente antes de ejecutarlo.
+
+    Args:
+        sql_query (str): Texto SQL (puede incluir espacios o punto y coma al final).
+        page_size (int): Número de filas por página.
+        project_id (str | None): ID del proyecto GCP (opcional si está configurado el entorno).
+
+    Yields:
+        list[dict]: Página de resultados (cada fila convertida a dict).
+    """
+    # 🧹 Limpieza del texto SQL
+    cleaned_query = (
+        sql_query.strip()  # elimina espacios y saltos al inicio y fin
+        .removeprefix("```sql").removeprefix("```").removesuffix("```")  # limpia delimitadores markdown si los hay
+        .strip()  # vuelve a limpiar por si quedaron espacios
+    )
+
+    # Quita el punto y coma final si lo tiene
+    if cleaned_query.endswith(";"):
+        cleaned_query = cleaned_query[:-1].strip()
+
+    # Inicializa el cliente
+    client = bigquery.Client(project=project_id)
+
+    # Ejecuta la consulta
+    query_job = client.query(cleaned_query)
+
+    # Devuelve resultados paginados
+    iterator = query_job.result(page_size=page_size)
+    for page in iterator.pages:
+        yield [dict(row) for row in page]
+
+
+if __name__ == "__main__":
+    resultado = consultar_bigquery_paginado("""```sql  
+SELECT  
+    Productos___Producto_NavId__description AS producto,  
+    Centros___Centro_NavId__name AS region,  
+    SUM(cantidad) AS total_vendido,  
+    SUM(cantidad * precioUnitario) AS ingreso_total  
+FROM  
+    `autingo-159109.rag_datasets.Objeto_Ventas`  
+WHERE  
+    Fecha >= DATE_SUB(CURRENT_DATE(), INTERVAL 3 MONTH)  
+GROUP BY  
+    producto, region  
+ORDER BY  
+    total_vendido DESC;  
+```""")
+    
+    for pagina in resultado:
+        for fila in pagina:
+            print(fila)
+        print("----- Fin de página -----")
@@ -0,0 +1,32 @@
+from decimal import Decimal    
+from datetime import datetime, date, time
+
+def convertir_decimales(obj):
+    if isinstance(obj, list):
+        return [convertir_decimales(x) for x in obj]
+    elif isinstance(obj, dict):
+        return {k: convertir_decimales(v) for k, v in obj.items()}
+    elif isinstance(obj, Decimal):
+        return float(obj)
+    else:
+        return obj
+    
+
+def convertir_fechas(obj):
+    """
+    Convierte objetos datetime, date y time a su representación ISO 8601.
+    """
+    if isinstance(obj, list):
+        return [convertir_fechas(x) for x in obj]
+    elif isinstance(obj, dict):
+        return {k: convertir_fechas(v) for k, v in obj.items()}
+    elif isinstance(obj, (datetime, date, time)):
+        return obj.isoformat()
+    else:
+        return obj
+    
+
+def limpiar_datos_para_json(data):
+    data = convertir_decimales(data)
+    data = convertir_fechas(data)
+    return data
@@ -280,6 +280,7 @@ version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
    { name = "agno" },
+    { name = "google-cloud-bigquery" },
    { name = "icecream" },
    { name = "marimo" },
    { name = "openai" },
@@ -292,6 +293,7 @@ dependencies = [
 [package.metadata]
 requires-dist = [
    { name = "agno", specifier = ">=2.1.1" },
+    { name = "google-cloud-bigquery", specifier = ">=3.38.0" },
    { name = "icecream", specifier = ">=2.1.8" },
    { name = "marimo", specifier = ">=0.16.5" },
    { name = "openai", specifier = ">=2.1.0" },
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+version = "2.25.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "google-auth" },
+    { name = "googleapis-common-protos" },
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+
+[package.optional-dependencies]
+grpc = [
+    { name = "grpcio" },
+    { name = "grpcio-status" },
+]
+
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