BigQuery 데이터 소스에 대해 작성된 컨텍스트로 에이전트 동작 안내

이 페이지에서는 BigQuery 데이터에 연결되는 Conversational Analytics API 데이터 에이전트를 위한 효과적인 프롬프트를 작성하기 위한 권장 구조를 설명합니다. 이러한 프롬프트는 system_instruction 매개변수를 사용하여 문자열로 정의하는 작성된 컨텍스트입니다.

시스템 요청 사항의 주요 구성요소 예시

다음 섹션에는 BigQuery의 시스템 안내 주요 구성요소의 예가 포함되어 있습니다. 이러한 키에는 다음이 포함됩니다.

이러한 주요 구성요소에 관한 설명은 작성된 컨텍스트로 에이전트 동작 안내 문서 페이지를 참고하세요.

tables로 데이터 설명하기

다음 YAML 코드 블록은 테이블 bigquery-public-data.thelook_ecommerce.orders의 tables 키의 기본 구조를 보여줍니다.

- 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

fields를 사용하여 일반적으로 사용되는 필드 설명

다음 샘플 YAML 코드는 orders 테이블의 order_id, status, created_at, num_of_items, earnings과 같은 주요 필드를 설명합니다.

- 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

measures로 비즈니스 측정항목 정의

예를 들어 다음과 같이 수입에서 비용을 뺀 값을 계산하는 profit 측정항목을 정의할 수 있습니다.

- tables:
    - table:
        - name: bigquery-public-data.thelook_ecommerce.orders
        - measures:
            - measure:
                - name: profit
                - description: Raw profit (earnings minus cost).
                - exp: earnings - cost
                - synonyms: gains

golden_queries로 정확도 개선

예를 들어 orders 테이블의 데이터에 대한 일반적인 분석의 골든 쿼리를 다음과 같이 정의할 수 있습니다.

- 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'

golden_action_plans로 여러 단계의 작업 개요 작성

예를 들어 연령대별 주문 분류를 표시하는 작업 계획을 정의하고 SQL 쿼리 및 시각화 관련 단계에 관한 세부정보를 포함할 수 있습니다.

- 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.

relationships로 테이블 조인 정의

예를 들어 bigquery-public-data.thelook_ecommerce.orders 테이블과 bigquery-public-data.thelook_ecommerce.users 테이블 간의 orders_to_user 관계를 다음과 같이 정의할 수 있습니다.

- 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

glossaries를 사용하여 비즈니스 용어 설명

예를 들어 다음과 같이 특정 비즈니스 컨텍스트에 따라 일반적인 비즈니스 상태 및 'OMPF'와 같은 용어를 정의할 수 있습니다.

- 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_descriptions에 추가 안내 포함

예를 들어 additional_descriptions 키를 사용하여 다음과 같이 조직에 관한 정보를 제공할 수 있습니다.

- 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.'

예: BigQuery의 시스템 안내

다음 예는 가상의 영업 분석가 에이전트에 대한 샘플 시스템 안내를 보여줍니다.

- 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.'