En esta página, se describe la estructura recomendada para escribir instrucciones eficaces para tus agentes de datos de la API de Conversational Analytics que se conectan a los datos de BigQuery. Estas instrucciones son contexto creado que defines como cadenas con el parámetro system_instruction.
Ejemplos de componentes clave de las instrucciones del sistema
En las siguientes secciones, se incluyen ejemplos de componentes clave de las instrucciones del sistema en BigQuery. Estas claves incluyen lo siguiente:
tablesfieldsmeasuresgolden_queriesgolden_action_plansrelationshipsglossariesadditional_descriptions
Para obtener descripciones de estos componentes clave, consulta la página de documentación Guía el comportamiento del agente con contexto creado.
Describe tus datos con tables
El siguiente bloque de código YAML muestra la estructura básica de la clave tables para la tabla bigquery-public-data.thelook_ecommerce.orders:
- tables:
- table:
- name: bigquery-public-data.thelook_ecommerce.orders
- description: Data for customer orders in The Look fictitious e-commerce store.
- synonyms:
- sales
- orders_data
- tags:
- ecommerce
- transaction
Describe los campos de uso común con fields
El siguiente código YAML de ejemplo describe los campos clave, como order_id, status, created_at, num_of_items y earnings para la tabla orders:
- tables:
- table:
- name: bigquery-public-data.thelook_ecommerce.orders
- fields:
- field:
- name: order_id
- description: The unique identifier for each customer order.
- field:
- name: user_id
- description: The unique identifier for each customer.
- field:
- name: status
- description: The current status of the order.
- sample_values:
- complete
- shipped
- returned
- field:
- name: created_at
- description: The timestamp when the order was created.
- field:
- name: num_of_items
- description: The total number of items in the order.
- aggregations:
- sum
- avg
- field:
- name: earnings
- description: The sales amount for the order.
- aggregations:
- sum
- avg
Define métricas comerciales con measures
Por ejemplo, puedes definir una métrica de profit como un cálculo de las ganancias menos el costo de la siguiente manera:
- tables:
- table:
- name: bigquery-public-data.thelook_ecommerce.orders
- measures:
- measure:
- name: profit
- description: Raw profit (earnings minus cost).
- exp: earnings - cost
- synonyms: gains
Mejora la precisión con golden_queries
Por ejemplo, puedes definir consultas de referencia para análisis comunes de los datos en la tabla orders de la siguiente manera:
- tables:
- table:
- golden_queries:
- golden_query:
- natural_language_query: How many orders are there?
- sql_query: SELECT COUNT(*) FROM sqlgen-testing.thelook_ecommerce.orders
- golden_query:
- natural_language_query: How many orders were shipped?
- sql_query: >-
SELECT COUNT(*) FROM sqlgen-testing.thelook_ecommerce.orders
WHERE status = 'shipped'
Cómo describir tareas de varios pasos con golden_action_plans
Por ejemplo, puedes definir un plan de acción para mostrar los desgloses de pedidos por grupo etario y, luego, incluir detalles sobre la consulta en SQL y los pasos relacionados con la visualización:
- tables:
- table:
- golden_action_plans:
- golden_action_plan:
- natural_language_query: Show me the number of orders broken down by age group.
- action_plan:
- step: >-
Run a SQL query that joins the table
sqlgen-testing.thelook_ecommerce.orders and
sqlgen-testing.thelook_ecommerce.users to get a
breakdown of order count by age group.
- step: >-
Create a vertical bar plot using the retrieved data,
with one bar per age group.
Define uniones de tablas con relationships
Por ejemplo, puedes definir una relación orders_to_user entre la tabla bigquery-public-data.thelook_ecommerce.orders y la tabla bigquery-public-data.thelook_ecommerce.users de la siguiente manera:
- relationships:
- relationship:
- name: orders_to_user
- description: >-
Connects customer order data to user information with the user_id and id fields to allow an aggregated view of sales by customer demographics.
- relationship_type: many-to-one
- join_type: left
- left_table: bigquery-public-data.thelook_ecommerce.orders
- right_table: bigquery-public-data.thelook_ecommerce.users
- relationship_columns:
- left_column: user_id
- right_column: id
Explicar los términos comerciales con glossaries
Por ejemplo, puedes definir términos como "estados comerciales comunes" y "OMPF" según el contexto específico de tu empresa de la siguiente manera:
- glossaries:
- glossary:
- term: complete
- description: Represents an order status where the order has been completed.
- synonyms: 'finish, done, fulfilled'
- glossary:
- term: shipped
- description: Represents an order status where the order has been shipped to the customer.
- glossary:
- term: returned
- description: Represents an order status where the customer has returned the order.
- glossary:
- term: OMPF
- description: Order Management and Product Fulfillment
Incluye más instrucciones con additional_descriptions
Por ejemplo, puedes usar la clave additional_descriptions para proporcionar información sobre tu organización de la siguiente manera:
- additional_descriptions:
- text: All the sales data pertains to The Look, a fictitious ecommerce store.
- text: 'Orders can be of three categories: food, clothes, and electronics.'
Ejemplo: Instrucciones del sistema en BigQuery
En el siguiente ejemplo, se muestran instrucciones del sistema de muestra para un agente analista de ventas ficticio de la siguiente manera:
- system_instruction: >-
You are an expert sales analyst for a fictitious ecommerce store. You will answer questions about sales, orders, and customer data. Your responses should be concise and data-driven.
- tables:
- table:
- name: bigquery-public-data.thelook_ecommerce.orders
- description: Data for orders in The Look, a fictitious ecommerce store.
- synonyms: sales
- tags: 'sale, order, sales_order'
- fields:
- field:
- name: order_id
- description: The unique identifier for each customer order.
- field:
- name: user_id
- description: The unique identifier for each customer.
- field:
- name: status
- description: The current status of the order.
- sample_values:
- complete
- shipped
- returned
- field:
- name: created_at
- description: >-
The date and time at which the order was created in timestamp
format.
- field:
- name: returned_at
- description: >-
The date and time at which the order was returned in timestamp
format.
- field:
- name: num_of_items
- description: The total number of items in the order.
- aggregations: 'sum, avg'
- field:
- name: earnings
- description: The sales revenue for the order.
- aggregations: 'sum, avg'
- field:
- name: cost
- description: The cost for the items in the order.
- aggregations: 'sum, avg'
- measures:
- measure:
- name: profit
- description: Raw profit (earnings minus cost).
- exp: earnings - cost
- synonyms: gains
- golden_queries:
- golden_query:
- natural_language_query: How many orders are there?
- sql_query: SELECT COUNT(*) FROM sqlgen-testing.thelook_ecommerce.orders
- golden_query:
- natural_language_query: How many orders were shipped?
- sql_query: >-
SELECT COUNT(*) FROM sqlgen-testing.thelook_ecommerce.orders
WHERE status = 'shipped'
- golden_action_plans:
- golden_action_plan:
- natural_language_query: Show me the number of orders broken down by age group.
- action_plan:
- step: >-
Run a SQL query that joins the table
sqlgen-testing.thelook_ecommerce.orders and
sqlgen-testing.thelook_ecommerce.users to get a
breakdown of order count by age group.
- step: >-
Create a vertical bar plot using the retrieved data,
with one bar per age group.
- table:
- name: bigquery-public-data.thelook_ecommerce.users
- description: Data for users in The Look, a fictitious ecommerce store.
- synonyms: customers
- tags: 'user, customer, buyer'
- fields:
- field:
- name: id
- description: The unique identifier for each user.
- field:
- name: first_name
- description: The first name of the user.
- tag: person
- sample_values: 'alex, izumi, nur'
- field:
- name: last_name
- description: The first name of the user.
- tag: person
- sample_values: 'warmer, stilles, smith'
- field:
- name: age_group
- description: The age demographic group of the user.
- sample_values:
- 18-24
- 25-34
- 35-49
- 50+
- field:
- name: email
- description: The email address of the user.
- tag: contact
- sample_values: '222larabrown@gmail.com, cloudysanfrancisco@gmail.com'
- golden_queries:
- golden_query:
- natural_language_query: How many unique customers are there?
- sql_query: >-
SELECT COUNT(DISTINCT id) FROM
bigquery-public-data.thelook_ecommerce.users
- golden_query:
- natural_language_query: How many users in the 25-34 age group have a cymbalgroup email address?
- sql_query: >-
SELECT COUNT(DISTINCT id) FROM
bigquery-public-data.thelook_ecommerce.users WHERE users.age_group =
'25-34' AND users.email LIKE '%@cymbalgroup.com';
- relationships:
- relationship:
- name: orders_to_user
- description: >-
Connects customer order data to user information with the user_id and id fields to allow an aggregated view of sales by customer demographics.
- relationship_type: many-to-one
- join_type: left
- left_table: bigquery-public-data.thelook_ecommerce.orders
- right_table: bigquery-public-data.thelook_ecommerce.users
- relationship_columns:
- left_column: user_id
- right_column: id
- glossaries:
- glossary:
- term: complete
- description: Represents an order status where the order has been completed.
- synonyms: 'finish, done, fulfilled'
- glossary:
- term: shipped
- description: Represents an order status where the order has been shipped to the customer.
- glossary:
- term: returned
- description: Represents an order status where the customer has returned the order.
- glossary:
- term: OMPF
- description: Order Management and Product Fulfillment
- additional_descriptions:
- text: All the sales data pertains to The Look, a fictitious ecommerce store.
- text: 'Orders can be of three categories: food, clothes, and electronics.'