Ejemplos de uso de Select AI
Explore la integración de la IA selecta de Oracle con varios proveedores de IA soportados para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chatear con el LLM.
- Ejemplo: selección de acciones de IA
Estos ejemplos ilustran acciones comunes de selección de IA. - Ejemplo: seleccionar IA con OCI Generative AI
En estos ejemplos se muestra cómo puede acceder a OCI Generative AI mediante su clave de API de OCI o entidad de recurso, crear un perfil de IA y generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chatear mediante los LLM de OCI Generative AI. - Ejemplo: selección de AI con OpenAI
Este ejemplo muestra cómo puede utilizar OpenAI para generar sentencias SQL a partir de peticiones de datos en lenguaje natural. - Ejemplo: selección de IA con Cohere
Este ejemplo muestra cómo puede utilizar Cohere para generar sentencias SQL a partir de peticiones de datos en lenguaje natural. - Ejemplo: selección de IA con el servicio OpenAI de Azure
En los siguientes ejemplos se muestra cómo puede activar el acceso al servicio OpenAI de Azure mediante su clave de API o utilizar el principal de servicio OpenAI de Azure, crear un perfil de IA y generar SQL a partir de peticiones de datos en lenguaje natural. - Ejemplo: selección de IA con Google
En este ejemplo, se muestra cómo puede utilizar Google para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chat mediante el LLM de Google Gemini. - Ejemplo: selección de IA con Anthropic
En este ejemplo, se muestra cómo puede utilizar Anthropic para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chatear con Anthropic Claude LLM. - Ejemplo: selección de IA con Hugging Face
Este ejemplo muestra cómo puede utilizar Hugging Face para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chat mediante el LLM Hugging Face. - Ejemplo: selección de IA con AWS
Este ejemplo muestra cómo puede utilizar AWS para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chatear con los modelos disponibles con AWS. - Ejemplo: selección de AI con proveedores compatibles con OpenAI
Este ejemplo muestra cómo puede utilizar proveedores compatibles con OpenAI para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chat mediante los modelos disponibles con proveedores compatibles con OpenAI. - Ejemplo: activación de conversaciones en Seleccionar IA
Estos ejemplos ilustran la activación de conversaciones en Seleccionar IA. - Ejemplo: configuración y uso de Select AI con RAG
Este ejemplo lo guía a través de la configuración de credenciales, la configuración del acceso a la red y la creación de un índice vectorial para integrar los servicios en la nube del almacén de vectores de OCI Generative AI con OpenAI mediante Oracle Autonomous AI Database. - Ejemplo: Seleccionar IA con modelos de transformadores en la base de datos
Este ejemplo muestra cómo puede importar un modelo de transformador previamente entrenado que esté almacenado en el almacenamiento de objetos de Oracle en su instancia de Oracle AI Database 26ai y, a continuación, utilizar el modelo importado en la base de datos en el perfil Select AI para generar incrustaciones vectoriales para fragmentos de documentos y peticiones de datos de usuario. - Ejemplo: Mejora de la Generación de Consultas SQL
Estos ejemplos muestran cómo los comentarios, las anotaciones, la clave ajena y las restricciones de integridad referencial en las tablas y columnas de la base de datos pueden mejorar la generación de consultas SQL a partir de peticiones de datos en lenguaje natural. - Ejemplo: generación de datos sintéticos
Este ejemplo explora cómo puede generar datos sintéticos que imiten las características y la distribución de datos reales. - Ejemplo: activación o desactivación del acceso a datos
En este ejemplo, se muestra cómo los administradores pueden controlar el acceso a datos y evitar que Select AI envíe tablas de esquema reales al LLM. - Ejemplo: selección de comentarios de IA
En estos ejemplos se muestra cómo puede utilizar el procedimientoDBMS_CLOUD_AI.FEEDBACKy los diferentes escenarios en los que se implica la acciónfeedbackpara proporcionar comentarios y mejorar la generación posterior de consultas SQL. - Ejemplo: Seleccionar resumen de IA
Estos ejemplos muestran cómo utilizar la acciónsummarizey la funciónDBMS_CLOUD_AI.SUMMARIZE. Además, personalice la generación de resumen para el contenido mediante la función. - Ejemplo: Seleccionar traducción de IA
En estos ejemplos se muestra cómo puede utilizar la capacidadtranslate. - Ejemplo: restricción del acceso a tablas en el perfil de AI
Este ejemplo muestra cómo restringir el acceso a tablas e indicar al LLM que utilice solo las tablas especificadas enobject_listdel perfil de AI. - Ejemplo: Especificar distinción entre mayúsculas y minúsculas para columnas
Este ejemplo muestra cómo puede definir la distinción entre mayúsculas y minúsculas para las columnas en el perfil AI.
Ejemplo: Seleccionar acciones de IA
Estos ejemplos ilustran acciones comunes de selección de IA.
En el siguiente ejemplo, se muestran acciones como runsql (valor por defecto), showsql, narrate, chat, explainsql, feedback y summarize que puede realizar con SELECT AI. Estos ejemplos utilizan el esquema sh con los atributos de perfil y proveedor de AI especificados en la función DBMS_CLOUD_AI.CREATE_PROFILE. Utilice las acciones Select AI después de definir el perfil AI mediante el procedimiento DBMS_CLOUD_AI.SET_PROFILE en la sesión actual.
Para generar un resumen del texto, utilice SELECT AI SUMMARIZE <TEXT>.
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai showsql how many customers exist;
RESPONSE
----------------------------------------------------
SELECT COUNT(*) AS total_customers
FROM SH.CUSTOMERS
SQL> select ai narrate how many customers exist;
RESPONSE
------------------------------------------------------
There are a total of 55,500 customers in the database.
SQL> select ai chat how many customers exist;
RESPONSE
--------------------------------------------------------------------------------
It is impossible to determine the exact number of customers that exist as it con
stantly changes due to various factors such as population growth, new businesses
, and customer turnover. Additionally, the term "customer" can refer to individu
als, businesses, or organizations, making it difficult to provide a specific num
ber.
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
-- Feedback on SQL Text
-- Negative feedback example:
SQL > select ai feedback for query "select ai showsql how many watch histories in total", please use sum instead of count;
-- Positive feedback example:
SQL > select ai feedback for query "select ai showsql how many watch histories in total", the sql query generated is correct;
-- Feedback on SQL ID
-- Negative feedback example:
SQL > select ai feedback please use sum instead of count for sql_id 1v1z68ra6r9zf;
-- Positive feedback example:
SQL > select ai feedback sql query result is correct for sql_id 1v1z68ra6r9zf;
-- If not specified, use default LASTAI SQL
-- To use default LASTAI sql, make sure that set server output off;
-- Negative feedback example:
SQL > select ai feedback please use ascending sorting for ranking;
-- Positive feedback example:
SQL > select ai feedback the result is correct;
SQL> SELECT AI SUMMARIZE
Like countless other people around the globe, I stream music, and like more
than six hundred million of them I mainly use Spotify. Streaming currently
accounts for about eighty per cent of the American recording industry’s
revenue, and in recent years Spotify’s health is often consulted as a measure
for the health of the music business over all. Last spring, the International
Federation of the Phonographic Industry reported global revenues of $28.6
billion, making for the ninth straight year of growth. All of this was
unimaginable in the two-thousands, when the major record labels appeared
poorly equipped to deal with piracy and the so-called death of physical media.
On the consumer side, the story looks even rosier. Adjusted for inflation, a
... (skipped 1000 rows in the middle)
Pelly writes of some artists, in search of viral fame, who surreptitiously use
social media to effectively beta test melodies and motifs, basically putting
together songs via crowdsourcing. Artists have always fretted about the
pressure to conform, but the data-driven, music-as-content era feels
different. “You are a Spotify employee at that point,” Daniel Lopatin, who
makes abstract electronic music as Oneohtrix Point Never, told Pelly. “If your
art practice is so ingrained in the brutal reality that Spotify has outlined
for all of us, then what is the music that you’re not making? What does the
music you’re not making sound like?” Listeners might wonder something similar.
What does the music we’re not hearing sound like?;
RESPONSE
------------------------------------------------------------------------------
The music streaming industry, led by Spotify, has revolutionized the way
people consume music, with streaming accounting for 80% of the American
recording industry's revenue. However, this shift has also complicated the
lives of artists, who struggle to survive in a hyper-abundant present where
music is often valued for its convenience rather than its artistic merit.
Spotify's algorithms prioritize popularity and profitability over artistic
diversity, leading to a homogenization of music and a devaluation of the
labor that goes into creating it. Meanwhile, the company's executives reap
enormous profits, with CEO Daniel Ek's net worth rivaling that of the
wealthiest musicians. As music critic Liz Pelly argues, the streaming economy
raises important questions about autonomy, creativity, and the value of art
in a world where everything is readily available and easily accessible.Tema principal: Ejemplos de uso de Select AI
Ejemplo: selección de IA con OCI Generative AI
En estos ejemplos se muestra cómo puede acceder a OCI Generative AI mediante su clave de API de OCI o entidad de recurso, crear un perfil de IA y generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chatear mediante los LLM de OCI Generative AI.
Nota
Si no especifica el parámetro
Si no especifica el parámetro
model_name, OCI Generative AI utiliza el modelo por defecto según la tabla en Seleccionar su proveedor de IA y LLM. Para obtener más información sobre los parámetros, consulte Atributos de perfil.
-- Create Credential with OCI API key
--
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaaa...',
tenancy_ocid => 'ocid1.tenancy.oc1..aaaa...',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GENAI',
attributes =>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('GENAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"GENAI"
--
-- Use AI
--
SQL> select ai how many customers exist;
Number of Customers
-------------------
55500
SQL> select ai how many customers in San Francisco are married;
COUNT(DISTINCTC."CUST_ID")
--------------------------
28
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c."CUST_ID")
FROM "SH"."CUSTOMERS" c
JOIN "SH"."COUNTRIES" co ON c."COUNTRY_ID" = co."COUNTRY_ID"
WHERE c."CUST_CITY" = 'San Francisco' AND c."CUST_MARITAL_STATUS" = 'married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
Here is the Oracle SQL query to find the number of customers in San Francisco who are married:
```
SELECT COUNT(*)
FROM "SH"."CUSTOMERS" c
WHERE c."CUST_CITY" = 'San Francisco'
AND c."CUST_MARITAL_STATUS" = 'Married';
```
Explanation:
* We use the `COUNT(*)` aggregate function to count the number of rows that match the conditions.
* We specify the table alias `c` for the `"SH"."CUSTOMERS"` table to make the query more readable.
* We use the `WHERE` clause to filter the rows based on two conditions:
+ `c."CUST_CITY" = 'San Francisco'`: We filter the rows where the customer's city is San Francisco.
+ `c."CUST_MARITAL_STATUS" = 'Married'`: We filter the rows where the customer's marital status is Married.
* The double quotes around the table and column names are used to make the query case-sensitive, as required in Oracle SQL.
Note: The column names and table names are assumed to be case-sensitive, so we enclose them in double quotes. If the column names and table names are not case-sensitive, you can remove the double quotes.
Collapse
This snippet was truncated for display; see it in full
SQL> select ai narrate how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------
There are 28 customers in San Francisco who are married.
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
An Autonomous AI Database is a type of database that uses artificial intelligence (AI) and machine learning (ML) to automate many of the administrative and maintenance tasks typically performed by a database administrator (DBA). This allows the database to manage itself, without human intervention, to a large extent.
Autonomous AI Databases are designed to be self-driving, self-securing, and self-repairing, which means they can:
1. **Automate administrative tasks**: Such as provisioning, patching, upgrading, and tuning, which frees up DBAs to focus on higher-level tasks.
2. **Optimize performance**: By automatically adjusting parameters, indexing, and caching to ensure optimal performance and efficiency.
3. **Detect and respond to security threats**: By using AI-powered security tools to identify and respond to potential security threats in real-time.
4. **Heal itself**: By automatically detecting and repairing errors, corruption, or other issues that may arise.
5. **Scale up or down**: To match changing workload demands, without the need for manual intervention.
The benefits of Autonomous AI Databases include:
1. **Increased efficiency**: By automating routine tasks, DBAs can focus on more strategic activities.
2. **Improved performance**: Autonomous AI Databases can optimize performance in real-time, leading to faster query response times and better overall system performance.
3. **Enhanced security**: AI-powered security tools can detect and respond to threats more quickly and effectively than human administrators.
4. **Reduced costs**: By minimizing the need for manual intervention, Autonomous AI Databases can help reduce labor costs and improve resource utilization.
5. **Improved reliability**: Autonomous AI Databases can detect and repair errors more quickly, reducing downtime and improving overall system reliability.
Oracle Autonomous AI Database is a popular example of an Autonomous AI Database, which was introduced in 2018. Other vendors, such as Amazon, Microsoft, and Google, also offer Autonomous AI Database services as part of their cloud offerings.
In summary, Autonomous AI Databases are designed to be self-managing, self-optimizing, and self-healing, which can lead to improved performance, security, and efficiency, while reducing costs and administrative burdens.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('GENAI');
PL/SQL procedure successfully completed.
Ejemplo: selección de IA con OCI Generative AI Resource Principal
Para utilizar la entidad de recurso con OCI Generative AI, el administrador del arrendamiento de Oracle Cloud Infrastructure debe otorgar acceso para los recursos de IA generativa a un grupo dinámico. Consulte Realización de requisitos previos para utilizar la entidad de recurso con una base de datos de IA autónoma para proporcionar acceso a un grupo dinámico.
Defina las políticas necesarias para obtener acceso a todos los recursos de IA generativa. Consulte Obtención de acceso a la IA generativa para obtener más información sobre las políticas de IA generativa.
-
Para obtener acceso a todos los recursos de IA generativa de todo el arrendamiento, utilice la siguiente política:
allow group <your-group-name> to manage generative-ai-family in tenancy -
Para obtener acceso a todos los recursos de IA generativa del compartimento, utilice la siguiente política:
allow group <your-group-name> to manage generative-ai-family in compartment <your-compartment-name>
Conéctese como administrador y active la entidad de recurso de OCI. Consulte ENABLE_PRINCIPAL_AUTH Procedure para configurar los parámetros.
Nota
Si no especifica el parámetro
Si no especifica el parámetro
model_name, OCI Generative AI utiliza el modelo por defecto según la tabla en Seleccionar su proveedor de IA y LLM. Para obtener más información sobre los parámetros, consulte Atributos de perfil.
-- Connect as Administrator user and enable OCI resource principal.
BEGIN
DBMS_CLOUD_ADMIN.ENABLE_PRINCIPAL_AUTH(provider => 'OCI');
END;
/
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GENAI',
attributes =>'{"provider": "oci",
"credential_name": "OCI$RESOURCE_PRINCIPAL",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('GENAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"GENAI"
--
-- Use AI
--
SQL> select ai how many customers exist;
Number of Customers
-------------------
55500
SQL> select ai how many customers in San Francisco are married;
COUNT(DISTINCTC."CUST_ID")
--------------------------
28
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c."CUST_ID")
FROM "SH"."CUSTOMERS" c
JOIN "SH"."COUNTRIES" co ON c."COUNTRY_ID" = co."COUNTRY_ID"
WHERE c."CUST_CITY" = 'San Francisco' AND c."CUST_MARITAL_STATUS" = 'married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
Here is the Oracle SQL query to find the number of customers in San Francisco who are married:
```
SELECT COUNT(*)
FROM "SH"."CUSTOMERS" c
WHERE c."CUST_CITY" = 'San Francisco'
AND c."CUST_MARITAL_STATUS" = 'Married';
```
Explanation:
* We use the `COUNT(*)` aggregate function to count the number of rows that match the conditions.
* We specify the table alias `c` for the `"SH"."CUSTOMERS"` table to make the query more readable.
* We use the `WHERE` clause to filter the rows based on two conditions:
+ `c."CUST_CITY" = 'San Francisco'`: We filter the rows where the customer's city is San Francisco.
+ `c."CUST_MARITAL_STATUS" = 'Married'`: We filter the rows where the customer's marital status is Married.
* The double quotes around the table and column names are used to make the query case-sensitive, as required in Oracle SQL.
Note: The column names and table names are assumed to be case-sensitive, so we enclose them in double quotes. If the column names and table names are not case-sensitive, you can remove the double quotes.
Collapse
This snippet was truncated for display; see it in full
SQL> select ai narrate how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------
There are 28 customers in San Francisco who are married.
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
An Autonomous AI Database is a type of database that uses artificial intelligence (AI) and machine learning (ML) to automate many of the administrative and maintenance tasks typically performed by a database administrator (DBA). This allows the database to manage itself, without human intervention, to a large extent.
Autonomous AI Databases are designed to be self-driving, self-securing, and self-repairing, which means they can:
1. **Automate administrative tasks**: Such as provisioning, patching, upgrading, and tuning, which frees up DBAs to focus on higher-level tasks.
2. **Optimize performance**: By automatically adjusting parameters, indexing, and caching to ensure optimal performance and efficiency.
3. **Detect and respond to security threats**: By using AI-powered security tools to identify and respond to potential security threats in real-time.
4. **Heal itself**: By automatically detecting and repairing errors, corruption, or other issues that may arise.
5. **Scale up or down**: To match changing workload demands, without the need for manual intervention.
The benefits of Autonomous AI Databases include:
1. **Increased efficiency**: By automating routine tasks, DBAs can focus on more strategic activities.
2. **Improved performance**: Autonomous AI Databases can optimize performance in real-time, leading to faster query response times and better overall system performance.
3. **Enhanced security**: AI-powered security tools can detect and respond to threats more quickly and effectively than human administrators.
4. **Reduced costs**: By minimizing the need for manual intervention, Autonomous AI Databases can help reduce labor costs and improve resource utilization.
5. **Improved reliability**: Autonomous AI Databases can detect and repair errors more quickly, reducing downtime and improving overall system reliability.
Oracle Autonomous AI Database is a popular example of an Autonomous AI Database, which was introduced in 2018. Other vendors, such as Amazon, Microsoft, and Google, also offer Autonomous AI Database services as part of their cloud offerings.
In summary, Autonomous AI Databases are designed to be self-managing, self-optimizing, and self-healing, which can lead to improved performance, security, and efficiency, while reducing costs and administrative burdens.
--
--Clear profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('GENAI');
PL/SQL procedure successfully completed.Ejemplo: especificación de una región diferente para el perfil de OCI Generative AI
En este ejemplo se muestra cómo especificar una región soportada de OCI Generative AI en su perfil. Consulte Regiones con IA generativa.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'GENAI',
attributes =>'{"provider": "oci",
"object_list": [
{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}
]
"region":"eu-frankfurt-1",
"model": "meta.llama-3.3-70b-instruct",
"credential_name": "GENAI_CRED",
"oci_compartment_id": "ocid1.compartment.oc1..."}');
END;
/Ejemplo: selección de IA con OCI Generative AI mediante el modelo Grok
En este ejemplo, se muestra cómo puede utilizar los modelos Grok de xAI con el soporte de OCI Generative AI. Revise Realizar requisitos previos para seleccionar IA.
--Create your AI Profile
BEGIN
DBMS_CLOUD_AI.create_profile(
profile_name =>'grok',
attributes =>'{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [ {"owner": "SH"}],
"oci_compartment_id": "ocid1.compartment.oc1..aaaaa...",
"model":"xai.grok-3"
}');
END;
/
PL/SQL procedure successfully completed.
--Set Profile
exec dbms_cloud_ai.set_profile('grok');
PL/SQL procedure successfully completed.
--Use Select AI
select ai how many customers exist;
TOTAL_CUSTOMERS
---------------
55500
select ai how many customers in San Francisco are married;
TOTAL_MARRIED_CUSTOMERS
-----------------------
46
select ai showsql how many customers in San Francisco are married;
RESPONSE
----------------------------------------------------------
SELECT COUNT(*) AS total_married_customers
FROM "SH"."CUSTOMERS" c
WHERE UPPER(c."CUST_CITY") = UPPER('San Francisco')
AND UPPER(c."CUST_MARITAL_STATUS") = UPPER('married')
select ai explainsql how many customers in San Francisco are married;
RESPONSE
-------------------
### Oracle SQL Query
```sql
SELECT COUNT(*) AS "Total_Married_Customers"
FROM "SH"."CUSTOMERS" "cust"
WHERE UPPER("cust"."CUST_CITY") = UPPER('San Francisco')
AND UPPER("cust"."CUST_MARITAL_STATUS") = UPPER('married')
```
### Detailed Explanation
1. **Table and Schema Naming**:
- The table `"CUSTOMERS"` is referenced with its schema name `"SH"` as `"SH"."CUSTOMERS"`. This ensures that the query explicitly points to the correct schema and table, avoiding ambiguity.
- A table alias `"cust"` is used for the `"CUSTOMERS"` table to make the query more readable and concise when referencing columns.
2. **Column Naming**:
- The result of the `COUNT(*)` function is aliased as `"Total_Married_Customers"` for clarity and readability. This descriptive name indicates exactly what the count represents.
- All column names (e.g., `"CUST_CITY"`, `"CUST_MARITAL_STATUS"`) are enclosed in double quotes to maintain case sensitivity as per Oracle's naming conventions when explicitly defined.
3. **String Comparison in WHERE Clause**:
- The strings 'San Francisco' and 'married' in the question are not enclosed in double quotes. As per the provided rules, case-insensitive comparison is required.
- Therefore, the `UPPER()` function is applied to both the column values (`"cust"."CUST_CITY"` and `"cust"."CUST_MARITAL_STATUS"`) and the literal strings ('San Francisco' and 'married') to ensure the comparison ignores case differences.
- This means that records with values like 'SAN FRANCISCO', 'san francisco', or any other case variation of 'San Francisco' will match, and similarly for 'married'.
4. **Purpose of the Query**:
- The query counts the total number of customers who are located in 'San Francisco' and have a marital status of 'married'.
- The `COUNT(*)` function is used to return the total number of rows that satisfy the conditions specified in the `WHERE` clause.
5. **Readability**:
- The query uses consistent formatting with indentation for the `WHERE` clause conditions to improve readability.
- The use of a meaningful alias (`"cust"`) and a descriptive result column name (`"Total_Married_Customers"`) makes the query easier to understand at a glance.
This query will return a single number representing the count of married customers in San Francisco, handling case variations in the data appropriately.
select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
I'm showing you the top 3 customers from San Francisco, based on their unique id
entification numbers, sorted from lowest to highest. Here's who they are:
- Lyndon Baltzer
- Mike Crocker
- Milburn Klemm
select ai chat what is Autonomous AI Database;
RESPONSE
-------------------
An **Autonomous AI Database** is a cloud-based database solution that uses artificial intelligence (AI) and machine learning (ML) to automate many of the routine tasks associated with managing and maintaining a database. It is designed to operate with minimal human intervention, allowing organizations to focus on data-driven insights and application development rather than database administration. The concept is often associated with Oracle's Autonomous AI Database, which was one of the first widely recognized implementations, but other cloud providers like AWS, Microsoft Azure, and Google Cloud also offer similar self-managing database services.
### Key Features of an Autonomous AI Database:
1. **Self-Driving:** Automates tasks such as provisioning, patching, tuning, and upgrading without manual input. It uses AI to optimize performance and adapt to workload changes in real-time.
2. **Self-Securing:** Automatically applies security updates, detects vulnerabilities, and protects against threats using built-in mechanisms like encryption and threat detection, reducing the risk of human error.
3. **Self-Repairing:** Identifies and resolves issues such as system failures or performance bottlenecks autonomously, ensuring high availability and minimizing downtime.
4. **Scalability:** Dynamically scales resources (compute and storage) up or down based on demand, optimizing cost and performance.
5. **Data Management:** Supports various data types and workloads, including transactional (OLTP), analytical (OLAP), and mixed workloads, often in a single converged database environment.
### Benefits:
- **Reduced Costs:** Minimizes the need for dedicated database administrators, lowering operational expenses.
- **Improved Performance:** AI-driven optimization ensures efficient query execution and resource allocation.
- **Enhanced Security:** Automated security features reduce the likelihood of breaches due to misconfigurations or delayed updates.
- **Increased Productivity:** Frees up IT staff to focus on innovation rather than routine maintenance.
### Use Cases:
- Businesses requiring high availability and reliability for critical applications.
- Organizations looking to modernize IT infrastructure with cloud-native solutions.
- Data analytics and machine learning projects needing scalable, optimized data storage.
- Environments where security and compliance are paramount, such as finance or healthcare.
### Example:
Oracle Autonomous AI Database, available on Oracle Cloud, offers two primary services:
- **Autonomous Transaction Processing (ATP):** Optimized for transactional workloads with high performance and reliability.
- **Autonomous AI Lakehouse:** Designed for analytics and big data workloads with automated data integration and optimization.
In essence, an Autonomous AI Database represents a shift toward intelligent, self-managing data systems that leverage automation and AI to simplify database operations, enhance security, and improve efficiency in a cloud environment. If you have a specific provider or context in mind, let me know, and I can dive deeper!Ejemplo: selección de IA con OCI Generative AI mediante el modelo LLAMA
En este ejemplo se muestra la función chat de OCI Generative AI. Destaca las capacidades del modelo a través de dos indicaciones: analizar los comentarios de los clientes para medir su sentimiento y generar un párrafo introductorio sobre la escalada en roca.
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaa',
tenancy_ocid => 'ocid1.tenancy.oc1..aaa',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
PL/SQL procedure successfully completed.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'GENAI',
attributes =>'{"provider": "oci",
"object_list": [
{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}
]
"model": "meta.llama-3.3-70b-instruct",
"oci_apiformat":"GENERIC",
"credential_name": "GENAI_CRED",
"oci_compartment_id": "ocid1.compartment.oc1..."}');
END;
/
PL/SQL procedure successfully completed.
--
--Set profile
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('GENAI');
PL/SQL procedure successfully completed.
SQL> set linesize 150
SQL> SELECT AI chat what is the sentiment of this comment I am not going to waste my time filling up this three page form. Lousy idea;
SQL>
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------
The sentiment of this comment is strongly negative. The user is expressing frustration and annoyance with the idea of filling out a three-page form, an
d is explicitly stating that they consider it a "lousy idea". The use of the phrase "waste my time" also implies that they feel the task is unnecessary
and unproductive. The tone is dismissive and critical.
SQL> SELECT AI chat Write an enthusiastic introductory paragraph on how to get started with rock climbing with Athletes as the target audience;
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------
Rock climbing is an exhilarating and challenging sport that's perfect for athletes looking to push their limits and test their strength, endurance,
and mental toughness. Whether you're a seasoned athlete or just starting out, rock climbing offers a unique and rewarding experience that will have
you hooked from the very first climb. With its combination of physical and mental challenges, rock climbing is a great way to build strength, improve
flexibility, and develop problem-solving skills. Plus, with the supportive community of climbers and the breathtaking views from the top of the climb,
you'll be hooked from the very first climb. So, if you're ready to take on a new challenge and experience the thrill of adventure, then it's time to
get started with rock climbing!Uso de OCI Generative AI con el modelo por defecto
En el siguiente ejemplo se utiliza el modelo de chat de OCI Generative AI por defecto. Si no especifica el parámetro model_name, OCI Generative AI utiliza el modelo por defecto según la tabla de selección del proveedor de IA y los LLM.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_DEFAULT',
attributes => '{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [{"owner": "ADB_USER"}]
}');
END;
/Uso de OCI Generative AI con modelo de chat
En el siguiente ejemplo se utiliza cohere.command-r-plus-08-2024 como modelo de chat de OCI Generative AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_COHERE_COMMAND_R_PLUS',
attributes => '{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [{"owner": "ADB_USER"}],
"model": "cohere.command-r-plus-08-2024"
}');
END;
/Uso de OCI Generative AI con ID de punto final de modelo de chat
En el siguiente ejemplo se muestra cómo especificar el ID de punto final del modelo de chat de OCI Generative AI en lugar de model. Si utiliza el ID de punto final del modelo de chat Meta Llama, especifique oci_apiformat como GENERIC.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_CHAT_ENDPOINT',
attributes => '{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [{"owner": "ADB_USER"}],
"oci_endpoint_id": "<endpoint_id>",
"oci_apiformat": "GENERIC"
}');
END;
/Uso de OCI Generative AI con OCID de modelo de chat
En este ejemplo se muestra cómo especificar el ID de punto final del modelo de chat de Cohere de OCI Generative AI en lugar de model. Si utiliza el ID de punto final del modelo de chat Meta Llama, especifique oci_apiformat como GENERIC.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_CHAT_OCID',
attributes => '{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [{"owner": "ADB_USER"}],
"model": "<model_ocid>",
"oci_apiformat": "COHERE"
}');
END;
/Tema principal: Ejemplos de uso de Select AI
Ejemplo: seleccione AI con OpenAI
En este ejemplo se muestra cómo puede utilizar OpenAI para generar sentencias SQL a partir de peticiones de datos en lenguaje natural.
Nota
Solo un DBA puede ejecutar los privilegios
Solo un DBA puede ejecutar los privilegios
EXECUTE y el procedimiento de ACL de red.
--Grants EXECUTE privilege to ADB_USER
--
SQL> grant execute on DBMS_CLOUD_AI to ADB_USER;
-- Grant Network ACL for OpenAI endpoint
--
SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.openai.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create Credential for AI provider
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'OPENAI_CRED',
username => 'OPENAI',
password => '<your_api_token>');
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OPENAI',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"OPENAI"
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous AI Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous AI Database uses machine learning and automation to optimize performance, security, and availability, allowing
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for
transactional workloads and Autonomous AI Lakehouse for analytical workloads. Autonomous AI Database provides high performance, scalability, and
reliability, making it an ideal choice for modern cloud-based applications.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('OPENAI');
PL/SQL procedure successfully completed.En el siguiente ejemplo, se muestra la especificación de un modelo diferente en el perfil de AI:
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OPENAI',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"model": "gpt-3.5-turbo",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/ Tema principal: Ejemplos de uso de Select AI
Ejemplo: Seleccionar IA con Cohere
En este ejemplo se muestra cómo puede utilizar Cohere para generar sentencias SQL a partir de peticiones de datos en lenguaje natural.
Nota
Solo un DBA puede ejecutar los privilegios
Solo un DBA puede ejecutar los privilegios
EXECUTE y el procedimiento de ACL de red.
--Grants EXECUTE privilege to ADB_USER
--
SQL>GRANT execute on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'COHERE_CRED',
username => 'COHERE',
password => 'your_api_token');
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Cohere endpoint
--
SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.cohere.ai',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'COHERE',
attributes =>'{"provider": "cohere",
"credential_name": "COHERE_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "sales"},
{"owner": "SH", "name": "products"},
{"owner": "SH", "name": "countries"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('COHERE');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"COHERE"
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('COHERE');
PL/SQL procedure successfully completed.En el siguiente ejemplo, se muestra cómo especificar un modelo diferente y atributos personalizados en su perfil de AI:
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'COHERE',
attributes =>
'{"provider": "cohere",
"credential_name": "COHERE_CRED",
"model": "cohere.command-a-03-2025",
"object_list": [{"owner": "ADB_USER"}],
"max_tokens":512,
"stop_tokens": [";"],
"temperature": 0.5,
"comments": true
}');
END;
/Tema principal: Ejemplos de uso de Select AI
Ejemplo: selección de IA con el servicio OpenAI de Azure
En los siguientes ejemplos se muestra cómo puede activar el acceso al servicio OpenAI de Azure mediante su clave de API o utilizar el principal de servicio OpenAI de Azure, crear un perfil de IA y generar SQL a partir de peticiones de datos en lenguaje natural.
-- Create Credential for AI integration
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'AZURE_CRED',
username => 'AZUREAI',
password => 'your_api_token');
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for OpenAI endpoint
--
BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => '<azure_resource_name>.openai.azure.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=> 'AZUREAI',
attributes=> '{"provider": "azure",
"azure_resource_name": "<azure_resource_name>",
"azure_deployment_name": "<azure_deployment_name>"
"credential_name": "AZURE_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"AZUREAI"
--
-- Use AI
--
select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
select ai chat what is Autonomous AI Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous AI Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous AI Database uses machine learning and automation to optimize performance, security, and availability, allowing
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous AI Lakehouse for analytical workloads. Autonomous AI Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.En el siguiente ejemplo, se muestra la especificación de un modelo diferente en el perfil de AI:
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'AZUREAI',
attributes=>'{"provider": "azure",
"credential_name": "AZURE$PA",
"model": "gpt-3.5-turbo",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"azure_resource_name": "<azure_resource_name>",
"azure_deployment_name": "<azure_deployment_name>"
}');
END;
/Ejemplo: selección de IA con principal de servicio OpenAI de Azure
Conéctese como administrador de base de datos para proporcionar acceso a la autenticación del principal de servicio de Azure y, a continuación, otorgue los permisos de ACL de red al usuario (ADB_USER) que desea utilizar Select AI. Para proporcionar acceso a los recursos de Azure, consulte Uso de principal de servicio de Azure para acceder a recursos de Azure.
Nota
Solo un usuario DBA puede ejecutar los privilegios
Solo un usuario DBA puede ejecutar los privilegios
EXECUTE y el procedimiento de ACL de red.
-- Connect as ADMIN user and enable Azure service principal authentication.
BEGIN
DBMS_CLOUD_ADMIN.ENABLE_PRINCIPAL_AUTH(provider => 'AZURE',
params => JSON_OBJECT('azure_tenantid' value 'azure_tenantid'));
END;
/
-- Copy the consent url from cloud_integrations view and consents the ADB-S application.
SQL> select param_value from CLOUD_INTEGRATIONS where param_name = 'azure_consent_url';
PARAM_VALUE
--------------------------------------------------------------------------------
https://login.microsoftonline.com/<tenant_id>/oauth2/v2.0/authorize?client_id=<client_id>&response_type=code&scope=User.read
-- On the Azure OpenAI IAM console, search for the Azure application name and assign the permission to the application.
-- You can get the application name in the cloud_integrations view.
SQL> select param_value from CLOUD_INTEGRATIONS where param_name = 'azure_app_name';
PARAM_VALUE
--------------------------------------------------------------------------------
ADBS_APP_DATABASE_OCID
--
-- Grant Network ACL for Azure OpenAI endpoint
--SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'azure_resource_name.openai.azure.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'AZUREAI',
attributes=>'{"provider": "azure",
"credential_name": "AZURE$PA",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"azure_resource_name": "<azure_resource_name>",
"azure_deployment_name": "<azure_deployment_name>"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"AZUREAI"
--
-- Use AI
--
select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
select ai chat what is Autonomous AI Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous AI Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous AI Database uses machine learning and automation to optimize performance, security, and availability, allowing us
ers to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous AI Lakehouse for analytical workloads. Autonomous AI Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
EXEC DBMS_CLOUD_AI.DROP_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.Tema principal: Ejemplos de uso de Select AI
Ejemplo: Seleccionar IA con Google
Este ejemplo muestra cómo puedes usar Google para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chatear usando el LLM de Google Gemini.
El siguiente ejemplo demuestra el uso de Google como proveedor de IA. En el ejemplo se muestra el uso de la clave de firma de la API de Google para proporcionar acceso a la red, crear un perfil de IA y utilizar las acciones Seleccionar IA para generar consultas SQL a partir de peticiones de datos en lenguaje natural y respuestas de chat.
--Grants EXECUTE privilege to ADB_USER
--
SQL> grant EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL> BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GOOGLE_CRED',
username => 'GOOGLE',
password => '<your_api_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Google endpoint
--
SQL>
SQL> BEGIN
DBMS_NETWORK_ACL_ADB_USER.APPEND_HOST_ACE(
host => 'generativelanguage.googleapis.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
SQL> BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('GOOGLE');
PL/SQL procedure successfully completed.
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous AI Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous AI Database uses machine learning and automation to optimize performance, security, and availability, allowing
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous Lakehouse for analytical workloads. Autonomous AI Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('GOOGLE');
PL/SQL procedure successfully completed. En el siguiente ejemplo, se muestra la especificación de un modelo diferente en el perfil de AI:
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"model": "gemini-1.5-pro",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/Tema principal: Ejemplos de uso de Select AI
Ejemplo: Seleccionar IA con antropía
En este ejemplo se muestra cómo puede utilizar Anthropic para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chatear mediante el LLM Anthropic Claude.
En el siguiente ejemplo, se muestra el uso de Anthropic como proveedor de IA. En el ejemplo, se muestra el uso de la clave de firma de la API antrópica para proporcionar acceso a la red, crear un perfil de IA y utilizar las acciones Seleccionar IA para generar consultas SQL a partir de peticiones de datos en lenguaje natural y chatear con el modelo de lenguaje natural Claude de Anthropic.
Consulte Atributos de perfil para proporcionar los atributos de perfil.
--Grant EXECUTE privilege to ADB_USER
SQL>GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL>BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'ANTHROPIC_CRED',
username => 'ANTHROPIC',
password => '<your api key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Anthropic endpoint
--
SQL>BEGIN
DBMS_NETWORK_ACL_ADB_USER.APPEND_HOST_ACE(
host => 'api.anthropic.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
SQL>BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'ANTHROPIC',
attributes =>'{"provider": "anthropic",
"credential_name": "ANTHROPIC_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL>EXEC DBMS_CLOUD_AI.SET_PROFILE('ANTHROPIC');
PL/SQL procedure successfully completed.
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous AI Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous AI Database uses machine learning and automation to optimize performance, security, and availability, allowing
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous AI Lakehouse for analytical workloads. Autonomous AI Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('ANTHROPIC');
PL/SQL procedure successfully completed.
En el siguiente ejemplo, se muestra la especificación de un modelo diferente en el perfil de AI:
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'ANTHROPIC',
attributes =>'{"provider": "anthropic",
"credential_name": "ANTHROPIC_CRED",
"model": "claude-3-opus-20240229",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/Tema principal: Ejemplos de uso de Select AI
Ejemplo: Seleccione AI con Hugging Face
En este ejemplo se muestra cómo puede utilizar Hugging Face para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chat mediante el LLM Hugging Face.
En el siguiente ejemplo, se muestra cómo usar Hugging Face como proveedor de IA. En el ejemplo se muestra el uso de la clave de firma de la API de Hugging Face para proporcionar acceso a la red, crear un perfil de IA y utilizar las acciones Select AI para generar consultas SQL a partir de peticiones de datos en lenguaje natural y chatear mediante el LLM de Hugging Face.
--Grant EXECUTE privilege to ADB_USER
SQL>GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL>BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'HF_CRED',
username => 'HF',
password => '<your_api_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Hugging Face endpoint
--
SQL>BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api-inference.huggingface.co',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
SQL>BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'HF',
attributes =>'{"provider": "huggingface",
"credential_name": "HF_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"model" : "Qwen/Qwen2.5-72B-Instruct"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL>EXEC DBMS_CLOUD_AI.SET_PROFILE('HF');
PL/SQL procedure successfully completed.
--
-- Use AI
--SQL> select ai how many customers exist;
Customer_Count
--------------
55500
SQL> select ai how many customers in San Francisco are married;
Married_Customers
-----------------
46
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
------------------------------------------------------------
SELECT COUNT("CUST_ID") AS "Married_Customers"
FROM "SH"."CUSTOMERS" "C"
WHERE "CUST_CITY" = 'San Francisco' AND "CUST_MARITAL_STATUS
" = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
------------------------------------------------------------
To answer the question "How many customers in San Francisco are married?", we need to query the "SH"."CUSTOMERS" table and filter the results based on the city and marital status.
We will use table aliases to make the query more readable and ensure that the string comparisons follow the specified rules.
Here is the Oracle SQL query:
```sql
SELECT COUNT("CUST_ID") AS "Married_Customers_in_San_Francisco"
FROM "SH"."CUSTOMERS" c
WHERE c."CUST_CITY" = 'San Francisco' -- Case insensitive comparison
AND UPPER(c."CUST_MARITAL_STATUS") = UPPER('Married'); --
Case insensitive comparison
```
### Explanation:
1. **Table Alias**:
RESPONSE
------------------------------------------------------------
- We use the alias `c` for the "SH"."CUSTOMERS" table to make the query more readable.
2. **Column Selection**:
- We select the count of "CUST_ID" to get the number of customers who meet the criteria. We alias this count as "Married_Customers_in_San_Francisco" for clarity.
3. **WHERE Clause**:
- **City Filter**: `c."CUST_CITY" = 'San Francisco'`
- Since 'San Francisco' is not in double quotes, we use a case-insensitive comparison.
- **Marital Status Filter**: `UPPER(c."CUST_MARITAL_STATUS") = UPPER('Married')`
- Since 'Married' is not in double quotes, we use a case-insensitive comparison by applying the `UPPER` function to both sides of the comparison.
This query will return the number of customers in San Francisco who are married.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
------------------------------------------------------------
The top 3 customers in San Francisco are:
- Hector Colven, with a customer ID of 8866.
- Milburn Klemm, with a customer ID of 1944.
- Gavin Xie, with a customer ID of 2579.
These customers have the highest total sales among all customers in San Francisco.
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
------------------------------------------------------------
An Autonomous AI Database is a type of database management system that uses artificial intelligence (AI) and machine learning (ML) to automate many of the routine tasks typically performed by database administrators (DBAs). These tasks include provisioning, patching, backups, tuning, and security. The goal of an Autonomous AI Database is to reduce the need for human intervention, minimize errors, and optimize performance,thereby allowing organizations to focus on more strategic activities.
### Key Features of Autonomous AI Databases:
RESPONSE
------------------------------------------------------------
1. **Automated Provisioning**: The database can be set up and configured automatically, reducing the time and effort required to get it up and running.
2. **Self-Patching and Updates**: The database automatically applies security patches and updates without downtime, ensuring that the system remains secure and up-to-date.
3. **Self-Tuning**: The database continuously monitors its performance and adjusts settings to optimize query execution and resource utilization.
4. **Self-Backup and Recovery**: Automated backup and recovery processes ensure that data is protected and can be restored quickly in the event of a failure.
5. **Security**: Advanced security features, including threat detection and response, are built into the database to protect against cyber threats.
6. **Scalability**: The database can automatically scale resources up or down based on demand, ensuring optimal performance and cost efficiency.
7. **Monitoring and Diagnostics**: Real-time monitoring and diagnostics help identify and resolve issues before they impact performance.
RESPONSE
------------------------------------------------------------
### Benefits of Autonomous AI Databases:
- **Reduced Operational Costs**: By automating routine tasks, the need for dedicated DBAs is reduced, lowering operational costs.
- **Improved Reliability**: Automated processes reduce the risk of human error, leading to more reliable and consistent performance.
- **Enhanced Security**: Continuous monitoring and automated security measures help protect against threats.
- **Faster Time to Market**: Automated provisioning and tuning allow applications to be deployed more quickly.
RESPONSE
------------------------------------------------------------
- **Scalability and Flexibility**: The ability to scale resources automatically ensures that the database can handle varying workloads efficiently.
### Use Cases:
- **Cloud Applications**: Autonomous AI Databases are particularly useful in cloud environments where scalability and reliability are critical.
- **Data Warehousing**: They can handle large volumes of data and complex queries, making them ideal for data warehousing and analytics.
RESPONSE
------------------------------------------------------------
- **IoT and Real-Time Data Processing**: They can process and analyze real-time data from IoT devices efficiently.
- **E-commerce**: They can handle high transaction volumes and ensure fast response times for online shopping platforms.
### Examples of Autonomous AI Databases:
- **Oracle Autonomous AI Database**: One of the first and most well-known Autonomous AI Databases, offering both transactional
and data warehousing capabilities.
- **Amazon Aurora**: A managed relational database service that includes automated scaling, patching, and backups.
- **Microsoft Azure SQL Database Managed Instance**: Provides a high level of automation and management for SQL Server databases in the cloud.
- **Google Cloud Spanner**: A globally distributed, horizontally scalable relational database that is highly available and consistent.
Autonomous AI Databases represent a significant advancement in database technology, offering organizations a more efficient, secure, and cost-effective way to manage their data.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('HF');
PL/SQL procedure successfully completed.Tema principal: Ejemplos de uso de Select AI
Ejemplo: selección de IA con AWS
En este ejemplo, se muestra cómo puede utilizar AWS para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chatear con los modelos disponibles con AWS.
En el siguiente ejemplo se muestra cómo utilizar AWS como proveedor de IA con Amazon Bedrock y sus modelos básicos. El ejemplo muestra la creación de credenciales de AWS, el acceso a la red, la creación de un perfil de IA y el uso de acciones Select AI para generar consultas SQL a partir de peticiones de datos en lenguaje natural y el chat mediante los modelos básicos de AWS.
Para utilizar AWS, obtenga la clave de acceso, las claves secretas y el ID de modelo. Consulte Uso de AWS. Utilice el identificador de modelo como atributo model en el procedimiento DBMS_CLOUD_AI.CREATE_PROFILE. Debe especificar el atributo model explícitamente, ya que no se proporciona ningún modelo por defecto.
--Grant EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'AWS_CRED',
username => '<your_AWS_access_key>',
password => '<your_AWS_secret_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for AWS
--
BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'bedrock-runtime.us-east-1.amazonaws.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'AWS',
attributes =>'{"provider": "aws",
"credential_name": "AWS_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"model" : "anthropic.claude-v2",
"conversation" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('AWS');
PL/SQL procedure successfully completed.
--
-- Use AI
--
SELECT AI how many customers exist;
"RESPONSE"
"COUNT(*)"
55500
SELECT AI how many customers in San Francisco are married;
"RESPONSE"
"COUNT(*)"
46
SELECT AI showsql how many customers in San Francisco are married;
"RESPONSE"
"SELECT COUNT(*) AS "Number of Married Customers in San Francisco"
FROM "SH"."CUSTOMERS" C
WHERE UPPER(C."CUST_CITY") = UPPER('San Francisco')
AND UPPER(C."CUST_MARITAL_STATUS") = UPPER('Married')"
SELECT AI explainsql how many customers in San Francisco are married;
"RESPONSE""SELECT
COUNT(*) AS "Number of Married Customers in San Francisco"
FROM "SH"."CUSTOMERS" C
WHERE C."CUST_CITY" = 'San Francisco'
AND C."CUST_MARITAL_STATUS" = 'Married'
Explanation:
- Used table alias C for CUSTOMERS table
- Used easy to read column names like CUST_CITY, CUST_MARITAL_STATUS
- Enclosed table name, schema name and column names in double quotes
- Compared string values in WHERE clause without UPPER() since the values are not in double quotes
- Counted number of rows satisfying the condition and aliased the count as "Number of Married Customers in San Francisco""
SELECT AI narrate what are the top 3 customers in San Francisco;
"RESPONSE"
The top 3 customers in San Francisco ordered by credit limit in descending order are:
1. Bert Katz
2. Madallyn Ladd
3. Henrietta Snodgrass
SELECT AI chat what is Autonomous AI Database;
"RESPONSE"
"An Autonomous AI Database is a cloud database service provided by Oracle Corporation. Some key features of Oracle Autonomous AI Database include:
- Fully automated and self-driving - The database automatically upgrades, patches, tunes, and backs itself up without any human intervention required.
- Self-securing - The database uses machine learning to detect threats and automatically apply security updates.
- Self-repairing - The database monitors itself and automatically recovers from failures and errors without downtime.
- Self-scaling - The database automatically scales compute and storage resources up and down as needed to meet workload demands.
- Serverless - The database is accessed as a cloud service without having to manually provision any servers or infrastructure.
- High performance - The database uses Oracle's advanced automation and machine learning to continuously tune itself for high performance.
- Multiple workload support - Supports transaction processing, analytics, graph processing, etc in a single converged database.
- Fully managed - Oracle handles all the management and administration of the database. Users just load and access their data.
- Compatible - Supports common SQL and Oracle PL/SQL for easy migration from on-prem Oracle databases.
So in summary, an Oracle Autonomous AI Database is a fully automated, self-driving, self-securing, and self-repairing database provided as a simple cloud service. The automation provides high performance, elasticity, and availability with minimal human labor required."
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('AWS');
PL/SQL procedure successfully completed.Tema principal: Ejemplos de uso de Select AI
Ejemplo: selección de AI con proveedores compatibles con OpenAI
En este ejemplo se muestra cómo puede utilizar proveedores compatibles con OpenAI para generar, ejecutar y explicar SQL a partir de peticiones de datos en lenguaje natural o chat mediante los modelos disponibles con proveedores compatibles con OpenAI.
El siguiente ejemplo muestra cómo utilizar Fireworks AI como proveedor compatible con OpenAI. Demuestra cómo crear credenciales con su clave de firma de API de Fireworks AI, configurar el acceso a la red, crear un perfil de IA y utilizar las acciones Select AI para generar consultas SQL a partir de peticiones de datos en lenguaje natural y chatear con el modelo Fireworks AI LLM.
Para utilizar Fireworks AI, especifique provider_endpoint como atributo en el procedimiento DBMS_CLOUD_AI.CREATE_PROFILE en lugar del atributo provider. Consulte Uso de proveedores compatibles con OpenAI para obtener el atributo. Debe especificar el atributo model explícitamente, ya que no se proporciona ningún modelo por defecto.
--Grant EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'FIREWORKS_CRED',
username => 'FIREWORKS',
password => '<your_fireworksaiapi_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Fireworks AI endpoint
--
BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.fireworks.ai',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'FIREWORKS',
attributes =>'{
"credential_name": "FIREWORKS_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"model" : "accounts/fireworks/models/llama-v3p1-405b-instruct",
"provider_endpoint" : "api.fireworks.ai/inference",
"conversation" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('FIREWORKS');
PL/SQL procedure successfully completed.
--
-- Use AI
--
select ai how many customers exist;
"RESPONSE"
"COUNT(*)"
55500
select ai how many customers in San Francisco are married;
"RESPONSE"
"COUNT(*)"
46
select ai showsql how many customers in San Francisco are married;
"RESPONSE"
"SELECT COUNT(*)
FROM ""SH"".""CUSTOMERS"" ""c""
WHERE UPPER(""c"".""CUST_CITY"") = UPPER('San Francisco')
AND UPPER(""c"".""CUST_MARITAL_STATUS"") = UPPER('Married')"
select ai explainsql how many customers in San Francisco are married;
"RESPONSE"
"Here is the Oracle SQL query:
```sql
SELECT COUNT(*)
FROM ""SH"".""CUSTOMERS"" ""c""
WHERE UPPER(""c"".""CUST_CITY"") = UPPER('San Francisco')
AND UPPER(""c"".""CUST_MARITAL_STATUS"") = UPPER('Married')
```
Explanation:
* We use the table alias `""c""` to refer to the `""CUSTOMERS""` table.
* We enclose the schema name `""SH""`, table name `""CUSTOMERS""`, and column names `""CUST_CITY""` and `""CUST_MARITAL_STATUS""` in double quotes to ensure case sensitivity.
* In the `WHERE` clause, we use the `UPPER()` function to perform case-insensitive comparisons for the strings `'San Francisco'` and `'Married'`, since they are not enclosed in double quotes.
* The `COUNT(*)` function returns the number of rows in the result set, which represents the number of customers in San Francisco who are married."
select ai narrate what are the top 3 customers in San Francisco;
"RESPONSE"
The top 3 customers in San Francisco are:
1. Lyndon Baltzer
2. Mike Crocker
3. Milburn Klemm
These customers are identified by their unique customer IDs, which are used to track their information and interactions. The list shows the customer's first and last names.
select ai chat what is Autonomous AI Database;
"RESPONSE"
"An Autonomous AI Database is a type of database that uses artificial intelligence (AI) and machine learning (ML) to automate many of the tasks traditionally performed by database administrators (DBAs). The goal of an Autonomous AI Database is to provide a self-managing, self-securing, and self-repairing database that can operate with minimal human intervention.
Autonomous AI Databases use advanced algorithms and machine learning techniques to:
1. **Automate database administration**: Tasks such as patching, backups, and performance tuning are automated, freeing up DBAs to focus on higher-level tasks.
2. **Predict and prevent issues**: The database uses machine learning to predict and prevent issues such as downtime, data breaches, and performance problems.
3. **Optimize performance**: The database continuously monitors and optimizes its own performance, ensuring that it is running at peak efficiency.
4. **Enhance security**: Autonomous AI Databases use advanced security features, such as encryption and access controls, to protect data from unauthorized access.
5. **Improve data management**: Autonomous AI Databases can automatically manage data, including data ingestion, processing, and storage.
The benefits of Autonomous AI Databases include:
1. **Increased efficiency**: By automating routine tasks, Autonomous AI Databases can reduce the workload of DBAs and improve overall efficiency.
2. **Improved security**: Autonomous AI Databases can detect and respond to security threats in real-time, reducing the risk of data breaches.
3. **Enhanced performance**: Autonomous AI Databases can optimize their own performance, ensuring that applications run quickly and efficiently.
4. **Reduced costs**: By automating routine tasks and improving efficiency, Autonomous AI Databases can help reduce costs associated with database management.
Examples of Autonomous AI Databases include:
1. Oracle Autonomous AI Database
2. Microsoft Azure SQL Database
3. Amazon Aurora
4. Google Cloud SQL
Overall, Autonomous AI Databases represent a significant shift in the way databases are managed and maintained, using AI and ML to automate many of the tasks traditionally performed by DBAs."
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('FIREWORKS');
PL/SQL procedure successfully completed.Tema principal: Ejemplos de uso de Select AI
Ejemplo: activación de conversaciones en Seleccionar IA
Estos ejemplos ilustran la activación de conversaciones en Select AI.
Antes de empezar
Nota
Un usuario con privilegios de administrador (ADMIN) debe otorgar EXECUTE y activar la lista de control de acceso de red (ACL).
Conversaciones Basadas en Sesión
Cree su perfil de IA. Defina el atributo conversation en true en el perfil, esta acción incluye el contenido de interacciones o peticiones de datos anteriores, lo que podría incluir metadatos de esquema, y defina el perfil. Una vez que el perfil está activado, puede comenzar a tener conversaciones con sus datos. Utiliza el lenguaje natural para hacer preguntas y hacer un seguimiento según sea necesario.
--Grants EXECUTE privilege to ADB_USER
--
SQL> grant execute on DBMS_CLOUD_AI to ADB_USER;
-- Grant Network ACL for OpenAI endpoint
--
SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.openai.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create Credential for AI provider
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'OPENAI_CRED',
username => 'OPENAI',
password => '<your_api_token>');
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OPENAI',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"OPENAI"
--
-- Use AI
--
what are the total number of customers;
CUSTOMER_COUNT
--------------
55500
break out count of customers by country;
RESPONSE
-----------------
COUNTRY_NAME CUSTOMER_COUNT
Italy 7780
Brazil 832
Japan 624
United Kingdom 7557
Germany 8173
United States of America 18520
France 3833
Canada 2010
Spain 2039
China 712
Singapore 597
New Zealand 244
Poland 708
Australia 831
Argentina 403
Denmark 383
South Africa 88
Saudi Arabia 75
Turkey 91
what age group is most common;
RESPONSE
--------------------------------------------------------------------------------
AGE_GROUP CUSTOMER_COUNT
65+ 28226
select ai keep the top 5 customers and their country by their purchases and include a rank in the result;
RESPONSE
--------------------------------------------------------------------------------
RANK CUSTOMER_NAME COUNTRY PURCHASES
1 Abigail Ruddy Japan 276
2 Abigail Ruddy Italy 168
3 Abigail Ruddy Japan 74
3 Abner Robbinette Germany 74
5 Abner Everett France 68
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('OPENAI');
PL/SQL procedure successfully completed.Conversaciones personalizables
En los siguientes ejemplos se muestra el uso de la API de gestión de conversaciones que admite conversaciones personalizables. Para utilizar Select AI para varias conversaciones:
Puede crear y establecer la conversación de las siguientes formas:
- Creación de una conversación
- Establecer la conversación en la sesión de usuario actual
- Utilice
Select AI <action> <prompt>
- Utilice la función
DBMS_CLOUD_AI.CREATE_COVERSATIONy, a continuación, defina la conversación medianteDBMS_CLOUD_AI.SET_CONVERSATION_ID. - Llame directamente al procedimiento
DBMS_CLOUD_AI.CREATE_CONVERSATIONpara crear y definir la conversación en un solo paso.
En el siguiente ejemplo se muestra cómo crear una conversación mediante la función DBMS_CLOUD_AI.CREATE_COVERSATION y definirla mediante el procedimiento DBMS_CLOUD_AI.SET_CONVERSATION_ID.
SELECT DBMS_CLOUD_AI.CREATE_CONVERSATION; -- in 19c, run SELECT DBMS_CLOUD_AI.create_conversation FROM dual;
CREATE_CONVERSATION
------------------------------------
30C9DB6E-EA4D-AFBA-E063-9C6D46644B92
EXEC DBMS_CLOUD_AI.SET_CONVERSATION_ID('30C9DB6E-EA4D-AFBA-E063-9C6D46644B92');
PL/SQL procedure successfully completedEn el siguiente ejemplo, se muestra la ejecución del procedimiento DBMS_CLOUD_AI.CREATE_COVERSATION para crear y definir conversation_id directamente.
EXEC DBMS_CLOUD_AI.create_conversation;
PL/SQL procedure successfully completed.También puede personalizar los atributos de conversación, como los atributos title, description, retention_days y conversation_length.
SELECT DBMS_CLOUD_AI.CREATE_CONVERSATION(
attributes => '{"title":"My first conversation",
"description":"this is my first conversation",
"retention_days":5,
"conversation_length":5}');
CREATE_CONVERSATION
------------------------------------
38F8B874-7687-2A3F-E063-9C6D4664EC3APuede ver si existe una conversación determinada consultando la vista DBA/USER_CLOUD_AI_CONVERSATIONS.
-- Verify the setup
SELECT conversation_id, conversation_title, description, retention_days,
conversation_length FROM DBA_CLOUD_AI_CONVERSATIONS WHERE
conversation_id = '38F8B874-7687-2A3F-E063-9C6D4664EC3A';
CONVERSATION_ID CONVERSATION_TITLE DESCRIPTION RETENTION_DAYS CONVERSATION_LENGTH
------------------------------------ ----------------------------------------------- ---------------------------------- ------------------------------ -------------------
38F8B874-7687-2A3F-E063-9C6D4664EC3A My first conversation this is my first conversation +00005 00:00:00.000000 5
También puede verificar si se define una conversación llamando a la función DBMS_CLOUD_AI.GET_CONVERSATION_ID.
SELECT DBMS_CLOUD_AI.GET_CONVERSATION_ID;
--------------------------------------------------------------------------------
30C9DB6E-EA4F-AFBA-E063-9C6D46644B92Ejemplo: uso de conversaciones personalizables con Select AI
Después de crear y establecer la conversación y activar tu perfil de IA, puedes comenzar a interactuar con tus datos. Utiliza el lenguaje natural para hacer preguntas y hacer un seguimiento según sea necesario.
Utilice SELECT AI <ACTION> <PROMPT>.
SELECT AI CHAT What is the difference in weather between Seattle and San Francisco?;
RESPONSE
--------------------------------------------------------------------------------
Seattle and San Francisco are both located on the West Coast of the United State
s, but they have distinct weather patterns due to their unique geography and cli
mate conditions. Here are the main differences:
1. **Rainfall**: Seattle is known for its rainy reputation, with an average annu
al rainfall of around 37 inches (94 cm). San Francisco, on the other hand, recei
ves significantly less rainfall, with an average of around 20 inches (51 cm) per
year.
2. **Cloud Cover**: Seattle is often cloudy, with an average of 226 cloudy days
per year. San Francisco is also cloudy, but to a lesser extent, with an average
of 165 cloudy days per year.
......
SELECT AI CHAT Explain the difference again in one paragraph only.;
RESPONSE
--------------------------------------------------------------------------------
Seattle and San Francisco have different weather patterns despite both experienc
ing a mild oceanic climate. San Francisco tends to be slightly warmer, with aver
age temperatures ranging from 45?F to 67?F, and receives less rainfall, around 2
0 inches per year, mostly during winter. In contrast, Seattle is cooler, with te
mperatures ranging from 38?F to 64?F, and rainier, with around 37 inches of rain
fall per year, distributed throughout the year. San Francisco is also known for
its fog, particularly during summer, and receives more sunshine, around 160 sunn
y days per year, although it's often filtered through the fog. Overall, San Fran
cisco's weather is warmer and sunnier, with more pronounced seasonal variations,
while Seattle's is cooler and rainier, with more consistent temperatures throug
hout the year.Ejemplo: uso de la función GENERATE para comparar dos conversaciones
El siguiente ejemplo muestra cómo se usan dos conversaciones indistintamente para hacer preguntas y verificar respuestas precisas. Cada conversación comienza con una pregunta diferente centrada en la comparación. Más adelante, cuando se hace la misma pregunta de seguimiento en ambas conversaciones, cada una devuelve una respuesta diferente en función de su contexto anterior.
-- First conversation
SELECT DBMS_CLOUD_AI.CREATE_CONVERSATION;
CREATE_CONVERSATION
------------------------------------
30C9DB6E-EA4D-AFBA-E063-9C6D46644B92
-- Second conversation
SELECT DBMS_CLOUD_AI.CREATE_CONVERSATION;
CREATE_CONVERSATION
------------------------------------
30C9DB6E-EA4E-AFBA-E063-9C6D46644B92
-- Call generate using the first conversation.
SELECT DBMS_CLOUD_AI.GENERATE(
prompt => 'What is the difference in weather between Seattle and San Francisco?',
profile_name => 'GENAI',
action => 'CHAT',
params => '{"conversation_id":"30C9DB6E-EA4D-AFBA-E063-9C6D46644B92"}') AS RESPONSE;
RESPONSE
--------------------------------------------------------------------------------
Seattle and San Francisco, both located in the Pacific Northwest and Northern Ca
lifornia respectively, experience a mild oceanic climate. However, there are som
e notable differences in their weather patterns:
1. **Temperature**: San Francisco tends to be slightly warmer than Seattle, espe
cially during the summer months. San Francisco's average temperature ranges from
45?F (7?C) in winter to 67?F (19?C) in summer, while Seattle's average temperat
ure ranges from 38?F (3?C) in winter to 64?F (18?C) in summer.
2. **Rainfall**: Seattle is known for its rainy reputation, with an average annu
al rainfall of around 37 inches (94 cm). San Francisco receives less rainfall, w
ith an average of around 20 inches (51 cm) per year. However, San Francisco's ra
infall is more concentrated during the winter months, while Seattle's rainfall i
s more evenly distributed throughout the year.
......
-- Call generate using the second conversation.
SELECT DBMS_CLOUD_AI.GENERATE(
prompt => 'How does the cost of living compare between New York and Los Angeles?',
profile_name => 'GENAI',
action => 'CHAT',
params => '{"conversation_id":"30C9DB6E-EA4E-AFBA-E063-9C6D46644B92"}') AS RESPONSE;
RESPONSE
--------------------------------------------------------------------------------
The cost of living in New York and Los Angeles is relatively high compared to ot
her cities in the United States. However, there are some differences in the cost
of living between the two cities. Here's a comparison of the cost of living in
New York and Los Angeles:
1. Housing: The cost of housing is significantly higher in New York than in Los
Angeles. The median home price in New York is around $999,000, while in Los Ange
les it's around $849,000. Rent is also higher in New York, with the average rent
for a one-bedroom apartment being around $3,000 per month, compared to around $
2,400 per month in Los Angeles.
2. Food: The cost of food is relatively similar in both cities, with some variat
ion in the cost of certain types of cuisine. However, eating out in New York can
be more expensive, with the average cost of a meal at a mid-range restaurant be
ing around $15-20 per person, compared to around $12-18 per person in Los Angele
s.
......
-- Call generate using the first conversation.
SELECT DBMS_CLOUD_AI.GENERATE(
prompt => 'Explain the difference again in one paragraph only.',
profile_name => 'GENAI',
action => 'CHAT',
params => '{"conversation_id":"30C9DB6E-EA4D-AFBA-E063-9C6D46644B92"}') AS RESPONSE;
RESPONSE
--------------------------------------------------------------------------------
Seattle and San Francisco have different weather patterns despite both experienc
ing a mild oceanic climate. San Francisco tends to be slightly warmer, with aver
age temperatures ranging from 45?F to 67?F, and receives less rainfall, around 2
0 inches per year, mostly during winter. In contrast, Seattle is cooler, with te
mperatures ranging from 38?F to 64?F, and rainier, with around 37 inches of rain
fall per year, distributed throughout the year. San Francisco is also known for
its fog, particularly during summer, and receives more sunshine, around 160 sunn
y days per year, although it's often filtered through the fog. Overall, San Fran
cisco's weather is warmer and sunnier, with more pronounced seasonal variations,
while Seattle's is cooler and rainier, with more consistent temperatures throug
hout the year.
-- Call generate using the second conversation.
SELECT DBMS_CLOUD_AI.GENERATE(
prompt => 'Explain the difference again in one paragraph only.',
profile_name => 'GENAI',
action => 'CHAT',
params => '{"conversation_id":"30C9DB6E-EA4E-AFBA-E063-9C6D46644B92"}') AS RESPONSE;
RESPONSE
--------------------------------------------------------------------------------
The cost of living in New York is approximately 20-30% higher than in Los Angele
s, mainly due to the higher cost of housing and transportation. New York has a m
edian home price of around $999,000 and average rent of $3,000 per month for a o
ne-bedroom apartment, compared to Los Angeles' median home price of $849,000 and
average rent of $2,400 per month. While the cost of food and utilities is relat
ively similar in both cities, the cost of transportation is higher in Los Angele
s due to its car-centric culture, but the cost of public transportation is highe
r in New York. Overall, the total monthly expenses for a single person in New Yo
rk can range from $4,600, compared to around $4,050 in Los Angeles, making New Y
ork the more expensive city to live in.
Puede llamar a la función DBMS_CLOUD_AI.GENERATE sin especificar una conversación; sin embargo, en tales casos, no se debe esperar una respuesta significativa.
-- Ask SELECT AI using the second conversation.
SELECT DBMS_CLOUD_AI.GENERATE(
prompt => 'Explain the difference again in one paragraph only.',
profile_name => 'GENAI',
action => 'CHAT') AS RESPONSE;
RESPONSE
--------------------------------------------------------------------------------
There is no previous explanation to draw from, as this is the beginning of our c
onversation. If you would like to ask a question or provide a topic, I would be
happy to explain the differences related to it in one paragraph.
Ejemplo: Verificación de Conversaciones mediante Vistas DBMS_CLOUD_AI
Puede consultar las vistas de conversación DBMS_CLOUD_AI para revisar los detalles de conversación y petición de datos. Consulte DBMS_CLOUD_AI Vistas para obtener más información.
Nota
El
El
Las vistas con el prefijo DBA_ solo están disponibles para los usuarios con privilegios de administrador (ADMIN).
SELECT conversation_id, conversation_title, description FROM dba_cloud_ai_conversations;
CONVERSATION_ID
------------------------------------
CONVERSATION_TITLE
----------------------------------------------------------------------------------------------------
DESCRIPTION
--------------------------------------------------------------------------------
30C9DB6E-EA4D-AFBA-E063-9C6D46644B92
Seattle vs San Francisco Weather
The conversation discusses the comparison of weather patterns between Seattle an
d San Francisco, focusing on the differences in temperature, rainfall, fog, suns
hine, and seasonal variation between the two cities.
30C9DB6E-EA4E-AFBA-E063-9C6D46644B92
NY vs LA Cost Comparison
The conversation discusses and compares the cost of living in New York and Los A
ngeles, covering housing, food, transportation, utilities, and taxes to provide
an overall view of the expenses in both cities.
SELECT conversation_id, count(*) FROM dba_cloud_ai_conversation_prompts
GROUP BY conversation_id;
CONVERSATION_ID COUNT(*)
------------------------------------ ----------
30C9DB6E-EA4D-AFBA-E063-9C6D46644B92 2
30C9DB6E-EA4E-AFBA-E063-9C6D46644B92 2Ejemplo: Actualizar Detalles de Conversación
Puede actualizar title, description y retention_days de una conversación mediante el procedimiento DBMS_CLOUD_AI.UPDATE_CONVERSATION. Puede verificar la actualización consultando la vista de conversación DBMS_CLOUD_AI.
-- Update the second conversation's title, description and retention_days
SQL> EXEC DBMS_CLOUD_AI.update_conversation(conversation_id => '30C9DB6E-EA4E-AFBA-E063-9C6D46644B92',
attributes => '{"retention_days":20,
"description":"This a description",
"title":"a title",
"conversation_length":20}');
PL/SQL procedure successfully completed.
-- Verify the information for the second conversation
SQL> SELECT conversation_title, description, retention_days
FROM dba_cloud_ai_conversations
WHERE conversation_id = '30C9DB6E-EA4E-AFBA-E063-9C6D46644B92';
CONVERSATION_TITLE DESCRIPTION RETENTION_DAYS LENGTH
-------------------------- ------------------------------------ -------------- --------------
a title This a description 20 20Ejemplo: supresión de una petición de datos
Puede suprimir una petición de datos individual de las conversaciones y verificar la modificación consultando la vista de conversación DBMS_CLOUD_AI.
-- Find the latest prompt for first conversation
SELECT conversation_prompt_id FROM dba_cloud_ai_conversation_prompts
WHERE conversation_id = '30C9DB6E-EA4D-AFBA-E063-9C6D46644B92'
ORDER BY created DESC
FETCH FIRST ROW ONLY;
CONVERSATION_PROMPT_ID
------------------------------------
30C9DB6E-EA61-AFBA-E063-9C6D46644B92
-- Delete the prompt
EXEC DBMS_CLOUD_AI.DELETE_CONVERSATION_PROMPT('30C9DB6E-EA61-AFBA-E063-9C6D46644B92');
PL/SQL procedure successfully completed.
-- Verify if the prompt is deleted
SELECT conversation_prompt_id FROM dba_cloud_ai_conversation_prompts
WHERE conversation_id = '30C9DB6E-EA4D-AFBA-E063-9C6D46644B92';
-- Only one prompt now
CONVERSATION_PROMPT_ID
------------------------------------
30C9DB6E-EA5A-AFBA-E063-9C6D46644B92Ejemplo: Borrado de una conversación
Puede eliminar toda la conversación, lo que también quita todos los campos asociados a ella.
-- Delete the first conversation
EXEC DBMS_CLOUD_AI.DROP_CONVERSATION('30C9DB6E-EA4D-AFBA-E063-9C6D46644B92');
PL/SQL procedure successfully completed.
-- Verify if the conversation and its prompts are removed
SELECT conversation_id FROM dba_cloud_ai_conversations;
-- We only have the second conversation now
CONVERSATION_ID
------------------------------------
30C9DB6E-EA4E-AFBA-E063-9C6D46644B92
SELECT conversation_id, count(*) FROM dba_cloud_ai_conversation_prompts GROUP BY conversation_id;
-- We only have prompts in the second conversation
CONVERSATION_ID COUNT(*)
------------------------------------ ----------
30C9DB6E-EA4E-AFBA-E063-9C6D46644B92 2Tema principal: Ejemplos de uso de Select AI
Ejemplo: configuración y uso de Select AI con RAG
Este ejemplo le guiará a través de la configuración de credenciales, la configuración del acceso a la red y la creación de un índice vectorial para integrar los servicios en la nube de almacenamiento vectorial de OCI Generative AI con OpenAI mediante Oracle Autonomous AI Database.
La configuración concluye con la creación de un perfil de IA que utiliza el índice vectorial para mejorar las respuestas del LLM. Por último, en este ejemplo se utiliza la acción Select AI narrate, que devuelve una respuesta que se ha mejorado mediante la información de la base de datos vectorial especificada.
En el siguiente ejemplo se muestra la creación y consulta de índices vectoriales en Oracle AI Database 26ai.
--Grants EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--Grants EXECUTE privilege DBMS_CLOUD_PIPELINE to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_PIPELINE to ADB_USER;
-- Create the OpenAI credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OPENAI_CRED',
username => 'OPENAI_CRED',
password => '<your_api_key>'
);
END;
/
PL/SQL procedure successfully completed.
-- Append the OpenAI endpoint
BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.openai.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
-- Create the object store credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OCI_CRED',
username => '<your_username>',
password => '<OCI_profile_password>'
);
END;
/
PL/SQL procedure successfully completed.
-- Create the profile with the vector index.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'OPENAI_ORACLE',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"vector_index_name": "MY_INDEX",
"temperature": 0.2,
"max_tokens": 4096,
"model": "gpt-3.5-turbo-1106"
}');
END;
/
PL/SQL procedure successfully completed.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI_ORACLE');
PL/SQL procedure successfully completed.
-- create a vector index with the vector store name, object store location and
-- object store credential
BEGIN
DBMS_CLOUD_AI.CREATE_VECTOR_INDEX(
index_name => 'MY_INDEX',
attributes => '{"vector_db_provider": "oracle",
"location": "https://swiftobjectstorage.us-phoenix-1.oraclecloud.com/v1/my_namespace/my_bucket/my_data_folder",
"object_storage_credential_name": "OCI_CRED",
"profile_name": "OPENAI_ORACLE",
"vector_dimension": 1536,
"vector_distance_metric": "cosine",
"chunk_overlap":128,
"chunk_size":1024
}');
END;
/
PL/SQL procedure successfully completed.
-- After the vector index is populated, we can now query the index.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI_ORACLE');
PL/SQL procedure successfully completed.
-- Select AI answers the question with the knowledge available in the vector database.
set pages 1000
set linesize 150
SELECT AI narrate how can I deploy an oracle machine learning model;
RESPONSE
To deploy an Oracle Machine Learning model, you would first build your model within the Oracle database. Once your in-database models are built, they become immediately available for use, for instance, through a SQL query using the prediction operators built into the SQL language.
The model scoring, like model building, occurs directly in the database, eliminating the need for a separate engine or environment within which the model and corresponding algorithm code operate. You can also use models from a different schema (user account) if the appropriate permissions are in place.
Sources:
- Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt (https://objectstorage.../v1/my_namespace/my_bucket/my_data_folder/Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt)
- Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-Database-Machine-Learning-and-APEX.txt (https://objectstorage.../v1/my_namespace/my_bucket/my_data_folder/Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-Database-Machine-Learning-and-APEX.txt)
Tema principal: Ejemplos de uso de Select AI
Ejemplo: selección de IA con modelos de transformadores en la base de datos
En este ejemplo se muestra cómo importar un modelo de transformador previamente entrenado que se almacena en el almacenamiento de objetos de Oracle en la instancia de Oracle AI Database 26ai y, a continuación, utilizar el modelo importado en la base de datos en el perfil Select AI para generar incrustaciones vectoriales para fragmentos de documentos y peticiones de datos de usuario.
Para utilizar modelos de transformadores en la base de datos en su perfil Select AI, asegúrese de tener:
-
tu modelo previamente entrenado importado en tu instancia de Oracle AI Database 26ai.
-
Opcionalmente, acceso al almacenamiento de objetos de Oracle.
Importe un modelo de transformador previamente entrenado en Oracle AI Database 26ai desde Oracle Object Storage
Revise los pasos de Importar modelos previamente entrenados en formato ONNX para la generación de vectores dentro de la base de datos y el blog Modelo de generación de embebido predefinido para Oracle AI Database 26ai para importar un modelo de transformador previamente entrenado en la base de datos.
En el siguiente ejemplo se muestra cómo importar un modelo de transformador incluido desde el almacenamiento de objetos de Oracle a la base de datos y, a continuación, ver el modelo importado.
- Create a Directory object, or use an existing directory object
CREATE OR REPLACE DIRECTORY ONNX_DIR AS 'onnx_model';
-- Object storage bucket
VAR location_uri VARCHAR2(4000);
EXEC :location_uri := 'https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/eLddQappgBJ7jNi6Guz9m9LOtYe2u8LWY19GfgU8flFK4N9YgP4kTlrE9Px3pE12/n/adwc4pm/b/OML-Resources/o/';
-- Model file name
VAR file_name VARCHAR2(512);
EXEC :file_name := 'all_MiniLM_L12_v2.onnx';
-- Download ONNX model from object storage into the directory object
BEGIN
DBMS_CLOUD.GET_OBJECT(
credential_name => NULL,
directory_name => 'ONNX_DIR',
object_uri => :location_uri || :file_name);
END;
/
-- Load the ONNX model into the database
BEGIN
DBMS_VECTOR.LOAD_ONNX_MODEL(
directory => 'ONNX_DIR',
file_name => :file_name,
model_name => 'MY_ONNX_MODEL');
END;
/
-- Verify
SELECT model_name, algorithm, mining_function
FROM user_mining_models
WHERE model_name='MY_ONNX_MODEL';Uso de modelos de transformadores en la base de datos en perfiles de IA seleccionados
Estos ejemplos ilustran cómo utilizar modelos de transformadores en la base de datos dentro de un perfil Select AI. Un perfil está configurado solo para generar incrustaciones vectoriales, mientras que el otro soporta acciones Select AI y creación de índices vectoriales.
Revise Realizar requisitos previos para Select AI para completar los requisitos previos.
A continuación se muestra un ejemplo para generar incrustaciones vectoriales únicamente:
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'EMBEDDING_PROFILE',
attributes => '{"provider" : "database",
"embedding_model": "MY_ONNX_MODEL"}'
);
END;
/A continuación se muestra un ejemplo de acciones generales de selección de IA y generación de índices vectoriales donde puede especificar un proveedor de IA soportado. En este ejemplo se utilizan el perfil y las credenciales de OCI Gen AI. Consulte Seleccionar su proveedor de IA y LLM para obtener una lista de proveedores admitidos. Sin embargo, si desea utilizar un modelo de transformador en la base de datos para generar incrustaciones de vectores, utilice "database: <MY_ONNX_MODEL>" en el atributo embedding_model:
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaaa...',
tenancy_ocid => 'ocid1.tenancy.oc1..aaaa...',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"model": "meta.llama-3.3-70b-instruct",
"credential_name": "GENAI_CRED",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: MY_ONNX_MODEL"}'
);
END;
/Uso de Select AI con un modelo de transformador en la base de datos de otro esquema
En este ejemplo se muestra cómo utilizar Select AI con un modelo de transformador en la base de datos si otro propietario de esquema es propietario del modelo. Especifique schema_name.object_name como nombre totalmente cualificado del modelo en el atributo embedding_model. Si el usuario actual es el propietario del esquema o es el propietario del modelo, puede omitir el nombre del esquema.
Asegúrese de tener los siguientes privilegios si un propietario de esquema diferente es propietario del modelo:
- Privilegio del sistema
CREATE ANY MINING MODEL - Privilegio del sistema
SELECT ANY MINING MODEL - Privilegio de objeto
SELECT MINING MODELen el modelo específico
Para otorgar un privilegio del sistema, debe tener otorgado el privilegio del sistema con ADMIN OPTION o se le ha otorgado el privilegio del sistema GRANT ANY PRIVILEGE.
Consulte System Privileges for Oracle Machine Learning for SQL para revisar los privilegios.
Las siguientes sentencias permiten a ADB_USER1 puntuar datos y ver detalles del modelo en cualquier esquema siempre que se haya otorgado acceso a los datos a SELECT. Sin embargo, ADB_USER1 solo puede crear modelos en el esquema ADB_USER1.
GRANT CREATE MINING MODEL TO ADB_USER1;
GRANT SELECT ANY MINING MODEL TO ADB_USER1;
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"credential_name": "GENAI_CRED",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: ADB_USER1.MY_ONNX_MODEL"}'
);
END;
/En el siguiente ejemplo se muestra cómo puede especificar el nombre de objeto del modelo sensible a mayúsculas/minúsculas:
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"credential_name": "GENAI_CRED",
"model": "meta.llama-3.3-70b-instruct",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: \"adb_user1\".\"my_model\""}'
);
END;
/Ejemplos integrales con diferentes proveedores de IA
Estos ejemplos demuestran los pasos integrales para utilizar el modelo de transformador en la base de datos con Select AI RAG. Un perfil utiliza database como provider creado exclusivamente para generar vectores de embebido, mientras que el otro perfil utiliza oci como provider creado para las acciones Select AI y el índice vectorial.
Revise Perform Prerequisites for Select AI para proporcionar los privilegios necesarios.
--Grant create any directory privilege to the user
GRANT CREATE ANY DIRECTORY to ADB_USER;
- Create a Directory object, or use an existing directory object
CREATE OR REPLACE DIRECTORY ONNX_DIR AS 'onnx_model';
-- Object storage bucket
VAR location_uri VARCHAR2(4000);
EXEC :location_uri := 'https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/eLddQappgBJ7jNi6Guz9m9LOtYe2u8LWY19GfgU8flFK4N9YgP4kTlrE9Px3pE12/n/adwc4pm/b/OML-Resources/o/';
-- Model file name
VAR file_name VARCHAR2(512);
EXEC :file_name := 'all_MiniLM_L12_v2.onnx';
-- Download ONNX model from object storage into the directory object
BEGIN
DBMS_CLOUD.GET_OBJECT(
credential_name => NULL,
directory_name => 'ONNX_DIR',
object_uri => :location_uri || :file_name);
END;
/
-- Load the ONNX model into the database
BEGIN
DBMS_VECTOR.LOAD_ONNX_MODEL(
directory => 'ONNX_DIR',
file_name => :file_name,
model_name => 'MY_ONNX_MODEL');
END;
/
-- Verify
SELECT model_name, algorithm, mining_function
FROM user_mining_models
WHERE model_name='MY_ONNX_MODEL';
--Administrator grants EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--Administrator grants EXECUTE privilege DBMS_CLOUD_PIPELINE to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_PIPELINE to ADB_USER;
-- Create the object store credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OCI_CRED',
username => '<your_username>',
password => '<OCI_profile_password>'
);
END;
/
PL/SQL procedure successfully completed.
-- Create the profile with Oracle Database.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'EMBEDDING_PROFILE',
attributes =>'{"provider": "database",
"embedding_model": "MY_ONNX_MODEL"
}');
END;
/
PL/SQL procedure successfully completed.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('EMBEDDING_PROFILE');
PL/SQL procedure successfully completed.
En este ejemplo se utiliza oci como provider.
--Grant create any directory privilege to the user
GRANT CREATE ANY DIRECTORY to ADB_USER;
- Create a Directory object, or use an existing directory object
CREATE OR REPLACE DIRECTORY ONNX_DIR AS 'onnx_model';
-- Object storage bucket
VAR location_uri VARCHAR2(4000);
EXEC :location_uri := 'https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/eLddQappgBJ7jNi6Guz9m9LOtYe2u8LWY19GfgU8flFK4N9YgP4kTlrE9Px3pE12/n/adwc4pm/b/OML-Resources/o/';
-- Model file name
VAR file_name VARCHAR2(512);
EXEC :file_name := 'all_MiniLM_L12_v2.onnx';
-- Download ONNX model from object storage into the directory object
BEGIN
DBMS_CLOUD.GET_OBJECT(
credential_name => NULL,
directory_name => 'ONNX_DIR',
object_uri => :location_uri || :file_name);
END;
/
-- Load the ONNX model into the database
BEGIN
DBMS_VECTOR.LOAD_ONNX_MODEL(
directory => 'ONNX_DIR',
file_name => :file_name,
model_name => 'MY_ONNX_MODEL');
END;
/
-- Verify
SELECT model_name, algorithm, mining_function
FROM user_mining_models
WHERE model_name='MY_ONNX_MODEL';
–-Administrator Grants EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--Administrator Grants EXECUTE privilege DBMS_CLOUD_PIPELINE to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_PIPELINE to ADB_USER;
-- Create the object store credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OCI_CRED',
username => '<your_username>',
password => '<OCI_profile_password>'
);
END;
/
--Create GenAI credentials
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaaa...',
tenancy_ocid => 'ocid1.tenancy.oc1..aaaa...',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
--Create OCI AI profile
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"model": "meta.llama-3.3-70b-instruct",
"credential_name": "GENAI_CRED",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: MY_ONNX_MODEL"}'
);
END;
/
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_GENAI');
PL/SQL procedure successfully completed.
-- create a vector index with the vector store name, object store location and
-- object store credential
BEGIN
DBMS_CLOUD_AI.CREATE_VECTOR_INDEX(
index_name => 'MY_INDEX',
attributes => '{"vector_db_provider": "oracle",
"location": "https://swiftobjectstorage.us-phoenix-1.oraclecloud.com/v1/my_namespace/my_bucket/my_data_folder",
"object_storage_credential_name": "OCI_CRED",
"profile_name": "OCI_GENAI",
"vector_dimension": 384,
"vector_distance_metric": "cosine",
"chunk_overlap":128,
"chunk_size":1024
}');
END;
/
PL/SQL procedure successfully completed.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_GENAI');
PL/SQL procedure successfully completed.
-- Select AI answers the question with the knowledge available in the vector database.
set pages 1000
set linesize 150
SELECT AI narrate how can I deploy an oracle machine learning model;
RESPONSE
To deploy an Oracle Machine Learning model, you would first build your model within the Oracle database. Once your in-database models are
built, they become immediately available for use, for instance, through a SQL query using the prediction operators built into the SQL
language.
The model scoring, like model building, occurs directly in the database, eliminating the need for a separate engine or environment within
which the model and corresponding algorithm code operate. You can also use models from a different schema (user account) if the appropriate
permissions are in place.
Sources:
- Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt (https://objectstorage.../v1/my_namespace/my_bucket/
my_data_folder/Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt)
- Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-Database-Machine-Learning-and-APEX.txt
(https://objectstorage.../v1/my_namespace/my_bucket/my_data_folder/Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-
Database-Machine-Learning-and-APEX.txt)Tema principal: Ejemplos de uso de Select AI
Ejemplo: Mejora de la Generación de Consultas SQL
Estos ejemplos demuestran cómo los comentarios, las anotaciones, la clave ajena y las restricciones de integridad referencial en las tablas y columnas de la base de datos pueden mejorar la generación de consultas SQL a partir de peticiones de datos en lenguaje natural.
Ejemplo: Mejora de la Generación de SQL con Comentarios de Tabla y Columna
Si tiene comentarios de tabla y columna en las tablas de la base de datos, active el parámetro "comments":"true" en la función DBMS_CLOUD_AI.CREATE_PROFILE para recuperar comentarios de nivel de tabla y de columna. Los comentarios se agregan a los metadatos del LLM para una mejor generación de SQL.
-- Adding comments to table 1, table 2, and table 3. Table 1 has 3 columns, table 2 has 7 columns, table 3 has 2 columns.
-- TABLE1
COMMENT ON TABLE table1 IS 'Contains movies, movie titles and the year it was released';
COMMENT ON COLUMN table1.c1 IS 'movie ids. Use this column to join to other tables';
COMMENT ON COLUMN table1.c2 IS 'movie titles';
COMMENT ON COLUMN table1.c3 IS 'year the movie was released';
-- TABLE2
COMMENT ON TABLE table2 IS 'transactions for movie views - also known as streams';
COMMENT ON COLUMN table2.c1 IS 'day the movie was streamed';
COMMENT ON COLUMN table2.c2 IS 'genre ids. Use this column to join to other tables';
COMMENT ON COLUMN table2.c3 IS 'movie ids. Use this column to join to other tables';
COMMENT ON COLUMN table2.c4 IS 'customer ids. Use this column to join to other tables';
COMMENT ON COLUMN table2.c5 IS 'device used to stream, watch or view the movie';
COMMENT ON COLUMN table2.c6 IS 'sales from the movie';
COMMENT ON COLUMN table2.c7 IS 'number of views, watched, streamed';
-- TABLE3
COMMENT ON TABLE table3 IS 'Contains the genres';
COMMENT ON COLUMN table3.c1 IS 'genre id. use this column to join to other tables';
COMMENT ON COLUMN table3.c2 IS 'name of the genre';
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'myprofile',
attributes =>
'{"provider": "azure",
"azure_resource_name": "my_resource",
"azure_deployment_name": "my_deployment",
"credential_name": "my_credential",
"comments":"true",
"object_list": [
{"owner": "moviestream", "name": "table1"},
{"owner": "moviestream", "name": "table2"},
{"owner": " moviestream", "name": "table3"}
]
}'
);
DBMS_CLOUD_AI.SET_PROFILE(
profile_name => 'myprofile'
);
END;
/
--Prompts
select ai what are our total views;
RESPONSE
-------------------------------------------------
TOTAL_VIEWS
-----------
97890562
select ai showsql what are our total views;
RESPONSE
-------------------------------------------------------------------------
SELECT SUM(QUANTITY_SOLD) AS total_views
FROM "moviestream"."table"
select ai what are our total views broken out by device;
DEVICE TOTAL_VIEWS
-------------------------- -----------
mac 14719238
iphone 20793516
ipad 15890590
pc 14715169
galaxy 10587343
pixel 10593551
lenovo 5294239
fire 5296916
8 rows selected.
select ai showsql what are our total views broken out by device;
RESPONSE
---------------------------------------------------------------------------------------
SELECT DEVICE, COUNT(*) AS TOTAL_VIEWS
FROM "moviestream"."table"
GROUP BY DEVICEEjemplo: Mejora de la Generación de Consultas SQL con Anotaciones de Tabla y Columna
En este ejemplo se muestra la integración de anotaciones en Select AI, aplicable en Oracle AI Database 26ai. Las anotaciones se agregan a los metadatos que se envían al LLM.
Si tiene una tabla con anotaciones en el esquema, active "annotations":"true" en la función DBMS_CLOUD_AI.CREATE_PROFILE para indicar a Select AI que agregue anotaciones a los metadatos.
--
-- Annotations
--
CREATE TABLE emp2 (
empno NUMBER,
ename VARCHAR2(50) ANNOTATIONS (display 'lastname'),
salary NUMBER ANNOTATIONS ("person_salary", "column_hidden"),
deptno NUMBER ANNOTATIONS (display 'department')
)ANNOTATIONS (requires_audit 'yes', version '1.0', ówner 'HR Organization');
Table created.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'GOOGLE_ANNOTATIONS',
attributes => '{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "emp2"}],
"annotations" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('GOOGLE_ANNOTATIONS');
PL/SQL procedure successfully completed.Ejemplo: Mejora de la Generación de Consultas SQL con Restricciones de Clave Ajena y Clave Referencial
En este ejemplo, se demuestra la capacidad del LLM para generar condiciones JOIN precisas mediante la recuperación de las restricciones de clave ajena y clave referencial en los metadatos del LLM. Las restricciones de clave ajena y clave referencial proporcionan datos de relación estructurada entre las tablas y el LLM.
Active "constraints":"true" en la función DBMS_CLOUD_AI.CREATE_PROFILE para que Select AI recupere la clave ajena y la clave referencial.
--
-- Referential Constraints
--
CREATE TABLE dept_test (
deptno NUMBER PRIMARY KEY,
dname VARCHAR2(50)
);
Table created.
CREATE TABLE emp3 (
empno NUMBER PRIMARY KEY,
ename VARCHAR2(50),
salary NUMBER,
deptno NUMBER,
CONSTRAINT emp_dept_fk FOREIGN KEY (deptno) REFERENCES dept_test(deptno)
);
Table created.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'GOOGLE_CONSTRAINTS',
attribues =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "dept_test"},
{"owner": "ADB_USER", "name": "emp3"}],
"constraints" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('GOOGLE_CONSTRAINTS');
PL/SQL procedure successfully completed.Ejemplo: detección automática de metadatos de tablas relevantes
En estos ejemplos se muestra cómo Select AI detecta automáticamente las tablas relevantes y envía metadatos solo para las tablas específicas relevantes para la consulta en Oracle AI Database 26ai. Para activar esta función, defina object_list_mode en automated. Esto crea automáticamente un índice vectorial denominado <profile_name>_OBJECT_LIST_VECINDEX. El índice vectorial se inicializa con atributos y valores por defecto, como refresh_rate, similarity_threshold y match_limit. Puede modificar algunos de los atributos mediante DBMS_CLOUD_AI.UPDATE_VECTOR_INDEX. Consulte UPDATE_VECTOR_INDEX Procedure para obtener más información.
Un perfil está configurado para utilizar object_list para especificar el esquema o los objetos del esquema, mientras que el otro no especifica object_list. Sin embargo, se espera la misma construcción SQL.
Revise Realizar requisitos previos para Select AI para proporcionar acceso al paquete DBMS_CLOUD_AI y proporcionar acceso de red al proveedor de AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'OCI_AUTO',
attributes=>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"object_list": [{"owner": "SH"}],
"oci_compartment_id": "ocid1.compartment.oc1..aaaa...",
"model" : "meta.llama-3.3-70b-instruct"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_AUTO');
PL/SQL procedure successfully completed.
select ai showsql how many customers in San Francisco are married;
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c."CUST_ID") AS "NUMBER_OF_CUSTOMERS"
FROM "SH"."CUSTOMERS" c
WHERE UPPER(c."CUST_CITY") = UPPER('San Francisco')
AND UPPER(c."CUST_MARITAL_STATUS") = UPPER('married')En el siguiente ejemplo se compara el mismo escenario sin utilizar object_list. Si no especifica object_list, Select AI selecciona automáticamente todos los objetos disponibles para el esquema actual.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'OCI_AUTO1',
attributes=>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"oci_compartment_id": "ocid1.compartment.oc1..aaaa...",
"object_list_mode": "automated",
"model" : "meta.llama-3.3-70b-instruct"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_AUTO1');
PL/SQL procedure successfully completed.
select ai showsql how many customers in San Francisco are married?;
RESPONSE
----------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(c."CUST_ID") AS "Number_of_Customers"
FROM "SH"."CUSTOMERS" c
WHERE UPPER(c."CUST_CITY") = UPPER('San Francisco')
AND UPPER(c."CUST_MARITAL_STATUS") = UPPER('Married')Tema principal: Ejemplos de uso de Select AI
Ejemplo: generación de datos sintéticos
En este ejemplo se explora cómo se pueden generar datos sintéticos que imitan las características y la distribución de datos reales.
En el siguiente ejemplo se muestra cómo crear algunas tablas en el esquema, utilizar OCI Generative AI como proveedor de IA para crear un perfil de IA, sintetizar datos en esas tablas mediante la función DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA y consultar o generar respuestas a peticiones de datos en lenguaje natural con Select AI.
--Create tables or use cloned tables
CREATE TABLE ADB_USER.Director (
director_id INT PRIMARY KEY,
name VARCHAR(100)
);
CREATE TABLE ADB_USER.Movie (
movie_id INT PRIMARY KEY,
title VARCHAR(100),
release_date DATE,
genre VARCHAR(50),
director_id INT,
FOREIGN KEY (director_id) REFERENCES ADB_USER.Director(director_id)
);
CREATE TABLE ADB_USER.Actor (
actor_id INT PRIMARY KEY,
name VARCHAR(100)
);
CREATE TABLE ADB_USER.Movie_Actor (
movie_id INT,
actor_id INT,
PRIMARY KEY (movie_id, actor_id),
FOREIGN KEY (movie_id) REFERENCES ADB_USER.Movie(movie_id),
FOREIGN KEY (actor_id) REFERENCES ADB_USER.Actor(actor_id)
);
-- Create the GenAI credential
BEGIN
DBMS_CLOUD.create_credential(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1....',
tenancy_ocid => 'ocid1.tenancy.oc1....',
private_key => 'vZ6cO...',
fingerprint => '86:7d:...'
);
END;
/
-- Create a profile
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GENAI',
attributes =>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"object_list": [{"owner": "ADB_USER",
"oci_compartment_id": "ocid1.compartment.oc1...."}]
}');
END;
/
EXEC DBMS_CLOUD_AI.set_profile('GENAI');
-- Run the API for single table
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_name => 'Director',
owner_name => 'ADB_USER',
record_count => 5
);
END;
/
PL/SQL procedure successfully completed.
-- Query the table to see results
SQL> SELECT * FROM ADB_USER.Director;
DIRECTOR_ID NAME
----------- ----------------------------------------------------------------------------------------------------
1 John Smith
2 Emily Chen
3 Michael Brown
4 Sarah Taylor
5 David Lee
-- Or ask select ai to show the results
SQL> select ai how many directors are there;
NUMBER_OF_DIRECTORS
-------------------
5
Ejemplo: generación de datos sintéticos para varias tablas
Después de crear y definir el perfil de proveedor de AI, utilice DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA para generar datos para varias tablas. Puede consultar o utilizar Select AI para responder a las peticiones de datos en lenguaje natural.
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_list => '[{"owner": "ADB_USER", "name": "Director","record_count":5},
{"owner": "ADB_USER", "name": "Movie_Actor","record_count":5},
{"owner": "ADB_USER", "name": "Actor","record_count":10},
{"owner": "ADB_USER", "name": "Movie","record_count":5,"user_prompt":"all movies released in 2009"}]'
);
END;
/
PL/SQL procedure successfully completed.
-- Query the table to see results
SQL> select * from ADB_USER.Movie;
MOVIE_ID TITLE RELEASE_D GENRE DIRECTOR_ID
---------- -------------------------------------------------------- --------- --------------------------------------------------------------- -----------
1 The Dark Knight 15-JUL-09 Action 8
2 Inglourious Basterds 21-AUG-09 War 3
3 Up in the Air 04-SEP-09 Drama 6
4 The Hangover 05-JUN-09 Comedy 1
5 District 9 14-AUG-09 Science Fiction 10
-- Or ask select ai to show the results
SQL> select ai how many actors are there;
Number of Actors
----------------
10Ejemplo: guía de generación de datos sintéticos con filas de ejemplo
Para guiar al servicio de IA en la generación de datos sintéticos, puede seleccionar aleatoriamente registros existentes de una tabla. Por ejemplo, al agregar {"sample_rows": 5} al argumento params, puede enviar 5 filas de ejemplo de una tabla al proveedor de AI. Este ejemplo genera 10 filas adicionales basadas en las filas de ejemplo de la tabla Transactions.
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_name => 'Transactions',
owner_name => 'ADB_USER',
record_count => 10,
params => '{"sample_rows":5}'
);
END;
/Ejemplo: Personalización de la generación de datos sintéticos con peticiones de datos de usuario
El argumento user_prompt permite especificar reglas o requisitos adicionales para la generación de datos. Esto se puede aplicar a una sola tabla o como parte del argumento object_list para varias tablas. Por ejemplo, en las siguientes llamadas a DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA, la petición de datos indica a la IA que genere datos sintéticos sobre películas lanzadas en 2009.
-- Definition for the Movie table CREATE TABLE Movie
CREATE TABLE Movie (
movie_id INT PRIMARY KEY,
title VARCHAR(100),
release_date DATE,
genre VARCHAR(50),
director_id INT,
FOREIGN KEY (director_id) REFERENCES Director(director_id)
);
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_name => 'Movie',
owner_name => 'ADB_USER',
record_count => 10,
user_prompt => 'all movies are released in 2009',
params => '{"sample_rows":5}'
);
END;
/
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_list => '[{"owner": "ADB_USER", "name": "Director","record_count":5},
{"owner": "ADB_USER", "name": "Movie_Actor","record_count":5},
{"owner": "ADB_USER", "name": "Actor","record_count":10},
{"owner": "ADB_USER", "name": "Movie","record_count":5,"user_prompt":"all movies are released in 2009"}]'
);
END;
/Ejemplo: Mejora de la Calidad de Datos Sintéticos mediante Estadísticas de Tabla
Si una tabla tiene estadísticas de columna o se clona a partir de una base de datos que incluye metadatos, Select AI puede utilizar estas estadísticas para generar datos que se parezcan mucho a los datos originales o sean coherentes con ellos.
Para las columnas NUMBER, los valores superior e inferior de las estadísticas guían el rango de valores. Por ejemplo, si la columna SALARY de la tabla EMPLOYEES original oscila entre 1000 y 10000, los datos sintéticos de esta columna también estarán dentro de este rango.
Para las columnas con valores distintos, como una columna STATE con los valores CA, WA y TX, los datos sintéticos utilizarán estos valores específicos. Puede gestionar esta función mediante el parámetro {"table_statistics": true/false}. Por defecto, las estadísticas de la tabla están activadas.
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_name => 'Movie',
owner_name => 'ADB_USER',
record_count => 10,
user_prompt => 'all movies released in 2009',
params => '{"sample_rows":5,"table_statistics":true}'
);
END;
/Ejemplo: uso de comentarios de columna para guiar la generación de datos
Si existen comentarios de columna, Seleccionar IA los incluye automáticamente para proporcionar información adicional para el LLM durante la generación de datos. Por ejemplo, un comentario en la columna Status de una tabla de transacciones puede mostrar valores permitidos como successful, failed, pending, canceled y need manual check. También puede agregar comentarios para explicar más detalladamente la columna, lo que proporciona a los servicios de IA instrucciones o indicaciones más precisas para generar datos precisos. Por defecto, los comentarios están desactivados. Consulte Parámetros opcionales para obtener más información.
-- Use comment on column
COMMENT ON COLUMN Transaction.status IS 'the value for state should either be ''successful'', ''failed'', ''pending'' or ''canceled''';
/
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_name => 'employees',
owner_name => 'ADB_USER',
record_count => 10
params => '{"comments":true}'
);
END;
/Ejemplo: definición de valores únicos en la generación de datos sintéticos
Cuando se generan grandes cantidades de datos sintéticos con LLM, es probable que se produzcan valores duplicados. Para evitarlo, configure una restricción única en la columna correspondiente. Esto garantiza que Select AI ignore las filas con valores duplicados en la respuesta del LLM. Además, para restringir valores para determinadas columnas, puede utilizar user_prompt o agregar comentarios para especificar los valores permitidos, como limitar una columna STATE a CA, WA y TX.
-- Use 'user_prompt'
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_name => 'employees',
owner_name => 'ADB_USER',
user_prompt => 'the value for state should either be CA, WA, or TX',
record_count => 10
);
END;
/
-- Use comment on column
COMMENT ON COLUMN EMPLOYEES.state IS 'the value for state should either be CA, WA, or TX'
/Ejemplo: Mejora de la Generación de Datos Sintéticos mediante Procesamiento Paralelo
Para reducir el tiempo de ejecución, Select AI divide las tareas de generación de datos sintéticos en fragmentos más pequeños para las tablas sin claves primarias o con claves primarias numéricas. Estas tareas se ejecutan en paralelo, interactuando con el proveedor de IA para generar datos de manera más eficiente. El grado de paralelismo (DOP) en la base de datos, influenciado por el nivel de servicio de la base de datos de IA autónoma y la configuración de ECPU u OCPU, determina el número de registros de cada proceso de fragmento. La ejecución de tareas en paralelo generalmente mejora el rendimiento, especialmente cuando se generan grandes cantidades de datos en muchas tablas. Para gestionar el procesamiento paralelo de la generación de datos sintéticos, defina priority como un parámetro opcional. Consulte Optional Parameters.
Tema principal: Ejemplos de uso de Select AI
Ejemplo: activación o desactivación del acceso a datos
En este ejemplo, se muestra cómo los administradores pueden controlar el acceso a los datos y evitar que Select AI envíe tablas de esquema reales al LLM.
Desactivación del acceso a datos
Para restringir el acceso a las tablas de esquema, conéctese como administrador y ejecute el siguiente procedimiento.
EXEC DBMS_CLOUD_AI.DISABLE_DATA_ACCESS;
PL/SQL procedure successfully completed.
Desactivando límites de acceso a datos Seleccione la acción narrate de AI y la generación de datos sintéticos. La acción narrate y la generación de datos sintéticos generan un error.
Conéctese como usuario de base de datos, cree y configure su perfil de AI. Revise Realizar requisitos previos para Select AI para configurar su perfil de AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'DATA_ACCESS',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner":"SH"}]
}');
END;
/
EXEC DBMS_CLOUD_AI.SET_PROFILE('DATA_ACCESS');
select ai how many customers;
NUM_CUSTOMERS
55500
select ai narrate what are the top 3 customers in San Francisco;
ORA-20000: Data access is disabled for SELECT AI.
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD", line 2228
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD_AI", line 13157
ORA-06512: at line 1 https://docs.oracle.com/error-help/db/ora-20000/
The stored procedure 'raise_application_error' was called which causes this error to be generated
Error at Line: 1 Column: 6En el siguiente ejemplo, se muestran los errores que se disparan al intentar generar datos sintéticos.
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'DATA_ACCESS_SDG',
object_name => 'CUSTOMERS_NEW',
owner_name => 'ADB_USER,
record_count => 5
);
END;
/
ERROR at line 1:
ORA-20000: Data access is disabled for SELECT AI.
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD", line 2228
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD_AI", line 13401
ORA-06512: at line 2Activación del acceso a datos
En el siguiente ejemplo, se muestra la activación del acceso a datos. Inicie sesión como administrador y ejecute el siguiente procedimiento:
EXEC DBMS_CLOUD_AI.ENABLE_DATA_ACCESS;
PL/SQL procedure successfully completed.Conéctese como usuario de base de datos, cree y configure su perfil de AI. Revise Realizar requisitos previos para Select AI para configurar su perfil de AI. Ejecute la acción narrate y genere por separado datos sintéticos.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'DATA_ACCESS_NEW',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner":"SH"}]
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('DATA_ACCESS_NEW');
PL/SQL procedure successfully completed.
select ai how many customers;
NUM_CUSTOMERS
55500
select ai narrate what are the top 3 customers in San Francisco;
"RESPONSE"
"The top 3 customers in San Francisco are Cody Seto, Lauren Yaskovich, and Ian Mc"
En el siguiente ejemplo, se muestra la generación correcta de datos sintéticos después de activar el acceso a los datos.
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'DATA_ACCESS_SDG',
object_name => 'CUSTOMERS_NEW',
owner_name => 'ADB_USER',
record_count => 5
);
END;
/
PL/SQL procedure successfully completed.Tema principal: Ejemplos de uso de Select AI
Ejemplo: Seleccionar comentarios de IA
En estos ejemplos se muestra cómo puede utilizar el procedimiento DBMS_CLOUD_AI.FEEDBACK y los diferentes escenarios de participación de la acción feedback para proporcionar comentarios y mejorar la posterior generación de consultas SQL.
Antes de empezar
Ejemplo: proporcionar comentarios negativos
En el siguiente ejemplo se muestra cómo proporcionar correcciones al SQL generado como comentarios (comentarios negativos) mediante feedback_type como negative y proporcionar la consulta SQL.
Agregue sus comentarios al perfil de AI denominado OCI_FEEDBACK1 llamando al procedimiento DBMS_CLOUD_AI.FEEDBACK con el parámetro sql_text que contiene la petición de datos. Consulte el procedimiento FEEDBACK para obtener información sobre los atributos. A continuación, recupere las columnas content y attributes de la tabla <profile_name>_FEEDBACK_VECINDEX$VECTAB, que está enlazada a esa consulta SQL específica. Seleccione AI para crear automáticamente esta tabla vectorial cuando utilice por primera vez la función de comentarios. Consulte Índice de vector para FEEDBACK para obtener más información.
SQL> select ai showsql how many movies;
RESPONSE
------------------------------------------------------------------------
SELECT COUNT(m."MOVIE_ID") AS "Number of Movies" FROM "ADB_USER"."MOVIES" m
SQL> exec DBMS_CLOUD_AI.FEEDBACK(profile_name=>'OCI_FEEDBACK1', sql_text=> 'select ai showsql how many movies', feedback_type=> 'negative', response=>'SELECT SUM(1) FROM "ADB_USER"."MOVIES"');
PL/SQL procedure successfully completed.
SQL> select CONTENT, ATTRIBUTES from OCI_FEEDBACK1_FEEDBACK_VECINDEX$VECTAB where JSON_VALUE(attributes, '$.sql_text') = 'select ai showsql how many movies';
CONTENT
----------------------------------------------------------------------------------------------------
how many movies
ATTRIBUTES
----------------------------------------------------------------------------------------------------
{"response":"SELECT SUM(1) FROM \"ADB_USER\".\"MOVIES\"","feedback_type":"negative","sql_id":null,"sql_text":"select ai showsql how many movies","feedback_content":null}Ejemplo: proporcionar comentarios positivos
En el siguiente ejemplo se muestra cómo proporcionar su aprobación para que acepte y confirme el SQL generado (comentarios positivos) mediante feedback_type como positive.
En este ejemplo, la consulta recupera sql_id de la vista v$mapped_sql para la petición de datos determinada. Consulte V_MAPPED_SQL para obtener más información.
Agregue sus comentarios al perfil de AI denominado OCI_FEEDBACK1 llamando al procedimiento DBMS_CLOUD_AI.FEEDBACK con el parámetro sql_id. A continuación, recupere las columnas content y attributes de la tabla <profile_name>_FEEDBACK_VECINDEX$VECTAB, que está enlazada a esa consulta SQL específica. Seleccione AI para crear automáticamente esta tabla vectorial cuando utilice por primera vez la función de comentarios. Consulte Índice de vector para FEEDBACK para obtener más información.
SQL> select ai showsql how many distinct movie genres?;
RESPONSE
-----------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT g."GENRE_NAME") AS "Number of Movie Genres" FROM "ADB_USER"."GENRES" g
SQL> SELECT sql_id FROM v$mapped_sql WHERE sql_text = 'select ai showsql how many distinct movie genres?';
SQL_ID
-------------
852w8u83gktc1
SQL> exec DBMS_CLOUD_AI.FEEDBACK(profile_name=>'OCI_FEEDBACK1', sql_id=> '852w8u83gktc1', feedback_type=>'positive', operation=>'add');
PL/SQL procedure successfully completed.
SQL> SELECT content, attributes FROM OCI_FEEDBACK1_FEEDBACK_VECINDEX$VECTAB WHERE JSON_VALUE(attributes, '$.sql_id') ='852w8u83gktc1';
CONTENT
----------------------------------------------------------------------------------------------------
how many distinct movie genres?
ATTRIBUTES
----------------------------------------------------------------------------------------------------
{"response":"SELECT COUNT(DISTINCT g.\"GENRE_NAME\") AS \"Number of Movie Genres\" FROM \"ADB_USER\".\"GENRES\" g","feedback_type":"positive","sql_id":"852w8u83gktc1","sql_text":"select ai showsql how many distinct movie genres?","feedback_content":null}Ejemplo: adición o supresión de comentarios para el SQL generado
En el siguiente ejemplo se muestra cómo agregar o suprimir comentarios para el SQL generado especificando los parámetros del procedimiento
DBMS_CLOUD_AI.FEEDBACK. En este ejemplo se muestra el uso de sql_id y sql_text junto con otros parámetros.
Nota
La selección de IA solo permite una entrada de comentarios única para cada
La selección de IA solo permite una entrada de comentarios única para cada
sql_id. Si proporciona comentarios adicionales para el mismo sql_id, Select AI sustituye la entrada anterior por la nueva.
Consulte el procedimiento FEEDBACK para obtener más información sobre los parámetros.
EXEC DBMS_CLOUD_AI.FEEDBACK(profile_name=>'OCI_FEEDBACK1',
sql_id=> '852w8u83gktc1',
feedback_type=>'positive',
operation=>'add');
EXEC DBMS_CLOUD_AI.FEEDBACK(profile_name=>'OCI_FEEDBACK1',
sql_text=> 'select ai showsql how many movies',
feedback_type=> 'negative',
response=>'SELECT SUM(1) FROM "ADB_USER"."MOVIES"',
feedback_content=>'Use SUM instead of COUNT');
EXEC DBMS_CLOUD_AI.FEEDBACK(profile_name=>'OCI_FEEDBACK1',
sql_id=> '852w8u83gktc1',
operation=>'delete');Ejemplo: uso de la acción de comentarios con el último SQL de IA para proporcionar comentarios negativos
En este ejemplo se muestra el uso de la acción feedback para mejorar el SQL generado sugiriendo las modificaciones mediante el lenguaje natural.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'OCI_FEEDBACK1',
attributes=>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"oci_compartment_id": "ocid1.compartment.oc1..aaaa...",
"object_list": [{"owner": "ADB_USER", "name": "users"},
{"owner": "ADB_USER", "name": "movies"},
{"owner": "ADB_USER", "name": "genres"},
{"owner": "ADB_USER", "name": "watch_history"},
{"owner": "ADB_USER", "name": "movie_genres"},
{"owner": "ADB_USER", "name": "employees1"},
{"owner": "ADB_USER", "name": "employees2"}
]
}');
END;
/
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_FEEDBACK1');
PL/SQL procedure successfully completed.
select ai showsql rank movie duration;
RESPONSE
-------------------------------------------------------------------------------
SELECT "DURATION" AS "Movie Duration" FROM "ADB_USER"."MOVIES" ORDER BY "DURATION"
select ai feedback use ascending sorting;
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Based on your feedback, the SQL query for prompt "rank movie duration" is successfully refined. The refined SQL query as following:
SELECT m."DURATION" AS "Movie Duration" FROM "ADB_USER."MOVIES" m ORDER BY m."DURATION" ASC
select ai showsql rank the movie duration;
RESPONSE
-----------------------------------------------------------------------------------------
SELECT m."DURATION" AS "Movie Duration" FROM "ADB_USER."MOVIES" m ORDER BY m."DURATION" ASCEjemplo: uso de la acción de comentarios con el último SQL de IA para proporcionar comentarios positivos
En este ejemplo se muestra el uso de la acción feedback para aceptar el SQL generado mediante lenguaje natural.
--Positive feedback
select ai showsql which movies are comedy?;
RESPONSE
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT DISTINCT m."TITLE" AS "Movie Title" FROM "ADB_USER"."MOVIES" m INNER JOIN "ADB_USER"."MOVIE_GENRES" mg ON m."MOVIE_ID" = mg."MOVIE_ID" INNER JOIN "ADB_USER"."GENRES" g ON mg."GENRE_ID" = g."GENRE_ID" WHERE g."GENRE_NAME" = 'comedy'
select ai feedback this is correct;
RESPONSE
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Thank you for your positive feedback. The SQL query for prompt "which movies are comedy?" is correctly implemented and delivering the expected results. It will be referenced for future optimizations and improvements.
Select AI Feedback Action Referring SQL_IDEjemplo: uso de la acción de comentarios con SQL_ID para proporcionar comentarios
En este ejemplo se muestra el uso de SQL_ID con la acción feedback para proporcionar comentarios para una consulta SQL generada concreta. Puede obtener SQL_ID consultando la tabla v$MAPPED_SQL.
-- Query mentioned with SQL_ID
select ai showsql how many movies are in each genre;
RESPONSE
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT g."GENRE_NAME" AS "Genre Name", COUNT(m."MOVIE_ID") AS "Number of Movies" FROM "ADB_USER"."MOVIES" m INNER JOIN "ADB_USER"."MOVIE_GENRES" mg ON m."MOVIE_ID" = mg."MOVIE_ID" INNER JOIN "ADB_USER"."GENRES" g ON mg."GENRE_ID" = g."GENRE_ID" GROUP BY g."GENRE_NAME"
select sql_id from v$cloud_ai_sql where sql_text = 'select ai showsql how many movies are in each genre';
SQL_ID
-------------
8azkwc0hr87ga
select ai feedback for query with sql_id = '8azkwc0hr87ga', rank in descending sorting;
RESPONSE
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Based on your feedback, the SQL query for prompt "how many movies are in each genre" is successfully refined. The refined SQL query as following:
SELECT g."GENRE_NAME" AS "Genre Name", COUNT(m."MOVIE_ID") AS "Number of Movies"
FROM "ADB_USER"."MOVIES" m
INNER JOIN "ADB_USER"."MOVIE_GENRES" mg ON m."MOVIE_ID" = mg."MOVIE_ID"
INNER JOIN "ADB_USER"."GENRES" g ON mg."GENRE_ID" = g."GENRE_ID"
GROUP BY g."GENRE_NAME"
ORDER BY COUNT(m."MOVIE_ID") DESC
Ejemplo: uso de acción de comentarios con texto de consulta
En este ejemplo, se muestra la acción feedback para una consulta Select AI específica mediante la inclusión de la petición de datos Select AI entre comillas seguida de sus comentarios.
-Query mentioned with SQL_TEXT
select ai showsql how many watch history in total;
RESPONSE
----------------------------------------------------------------------------------
SELECT COUNT(w."WATCH_ID") AS "Total Watch History" FROM "ADB_USER"."WATCH_HISTORY" w
select ai feedback for query "select ai showsql how many watch history in total", name the column as total_watch;
RESPONSE
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Based on your feedback, the SQL query for prompt "how many watch history in total" is successfully refined. The refined SQL query as following:
SELECT COUNT(w."WATCH_ID") AS "total_watch" FROM "ADB_USER"."WATCH_HISTORY" wTema principal: Ejemplos de uso de Select AI
Ejemplo: Seleccionar resumen de IA
En estos ejemplos se muestra cómo utilizar la acción summarize y la función DBMS_CLOUD_AI.SUMMARIZE. Además, personalice la generación de resumen para el contenido mediante la función.
Antes de empezar
Ejemplo: Uso de la Acción Resumir en la Línea de Comandos SQL
En el siguiente ejemplo, se utiliza
SUMMARIZE como acción Seleccionar AI. Utilice SELECT AI SUMMARIZE <TEXT> en la línea de comandos SQL para generar un resumen del texto de entrada.
SELECT AI SUMMARIZE
Like countless other people around the globe, I stream music, and like more
than six hundred million of them I mainly use Spotify. Streaming currently
accounts for about eighty per cent of the American recording industry’s
revenue, and in recent years Spotify’s health is often consulted as a measure
for the health of the music business over all. Last spring, the International
Federation of the Phonographic Industry reported global revenues of $28.6
billion, making for the ninth straight year of growth. All of this was
unimaginable in the two-thousands, when the major record labels appeared
poorly equipped to deal with piracy and the so-called death of physical media.
On the consumer side, the story looks even rosier. Adjusted for inflation, a
... (skipped 1000 rows in the middle)
Pelly writes of some artists, in search of viral fame, who surreptitiously use
social media to effectively beta test melodies and motifs, basically putting
together songs via crowdsourcing. Artists have always fretted about the
pressure to conform, but the data-driven, music-as-content era feels
different. “You are a Spotify employee at that point,” Daniel Lopatin, who
makes abstract electronic music as Oneohtrix Point Never, told Pelly. “If your
art practice is so ingrained in the brutal reality that Spotify has outlined
for all of us, then what is the music that you’re not making? What does the
music you’re not making sound like?” Listeners might wonder something similar.
What does the music we’re not hearing sound like?;
RESPONSE
------------------------------------------------------------------------------
The music streaming industry, led by Spotify, has revolutionized the way
people consume music, with streaming accounting for 80% of the American
recording industry's revenue. However, this shift has also complicated the
lives of artists, who struggle to survive in a hyper-abundant present where
music is often valued for its convenience rather than its artistic merit.
Spotify's algorithms prioritize popularity and profitability over artistic
diversity, leading to a homogenization of music and a devaluation of the
labor that goes into creating it. Meanwhile, the company's executives reap
enormous profits, with CEO Daniel Ek's net worth rivaling that of the
wealthiest musicians. As music critic Liz Pelly argues, the streaming economy
raises important questions about autonomy, creativity, and the value of art
in a world where everything is readily available and easily accessible.Consejo:
En SQL*Plus, una comilla simple (
') se trata como un delimitador de cadena. Si el texto contiene comillas simples, escape la comilla duplicándola (' a '') o delimite el texto mediante el mecanismo de comillas q'[]'. Si el texto contiene comillas dobles vacías (""), delimite el texto mediante el mecanismo q'[]'. Por ejemplo:SELECT AI SUMMARIZE q'[this's a text]';Ejemplo: uso del procedimiento DBMS_CLOUD_AI.SUMMARIZE para generar un resumen
Estos ejemplos muestran la generación de un resumen mediante el uso de diferentes parámetros del procedimiento DBMS_CLOUD_AI.SUMMARIZE.
Puede generar un resumen a partir de más de 3000 palabras de texto almacenadas en OCI Object Storage especificando el enlace de Object Storage como el parámetro
location_uri y las credenciales de su cuenta en la nube como credential_name mediante DBMS_CLOUD_AI.SUMMARIZESELECT DBMS_CLOUD_AI.SUMMARIZE(
location_uri => 'https://objectstorage.ca-toronto-1.oraclecloud.com/n/' ||
'namespace-string/b/bucketname/o/data_folder/' ||
'summary/test_4000_words.txt',
credential_name => 'STORE_CRED',
profile_name => 'GENAI')
from DUAL;Otra forma de generar un resumen a partir de un texto almacenado en un almacenamiento de objetos de OCI es mediante el parámetro
content para llamar al procedimiento DBMS_CLOUD.GET_OBJECT.
SELECT DBMS_CLOUD_AI.SUMMARIZE(
content => TO_CLOB(
DBMS_CLOUD.GET_OBJECT(
credential_name => 'STORE_CRED',
location_uri => 'https://objectstorage.ca-toronto-1.oraclecloud.com/n/' ||
'namespace-string/b/bucketname/o/data_folder/' ||
'summary/test_4000_words.txt')),
profile_name => 'GENAI'>)
from DUAL;Ejemplo: generación de un resumen mediante la especificación de peticiones de datos de usuario, palabras mínimas y palabras máximas
El siguiente ejemplo muestra cómo generar un resumen de un texto de más de 3000 palabras especificando los siguientes parámetros:
user_prompt: el resumen debe empezar por 'El resumen del artículo es: 'min_words: 50max_words: 100
SELECT DBMS_CLOUD_AI.SUMMARIZE(
content => TO_CLOB(
DBMS_CLOUD.GET_OBJECT(
credential_name =>'STORE_CRED',
location_uri =>'https://objectstorage.ca-toronto-1.oraclecloud.com/n/' ||
'namespace-string/b/bucketname/o/data_folder/' ||
'summary/test_4000_words.txt')),
profile_name => 'GENAI',
user_prompt => 'The summary should start with ''The summary of ' ||
'the article is: ''',
params => '{"min_words":50,"max_words":100}')
As response FROM dual;
RESPONSE
--------------------------------------------------------------------------------
The summary of the article is: The music streaming industry, led by Spotify, has
revolutionized the way people consume music, with streaming accounting for abou
t eighty per cent of the American recording industry's revenue. However, this sh
ift has also raised concerns about the impact on artists, with many struggling t
o make a living due to low royalty rates and the dominance of playlists. The art
icle explores the history of music streaming, from the early days of Napster to
the current landscape, and how it has changed the way people listen to music. It
also delves into the issues of autonomy and creativity in the music industry, w
ith some artists feeling pressured to conform to certain styles or formulas to s
ucceed on platforms like Spotify. The article cites examples of artists who have
spoken out against the streaming economy, including Taylor Swift and Neil Young
, and discusses the rise of alternative platforms like Bandcamp and Nina. Ultima
tely, the article suggests that the streaming economy has created a perverse vis
ion for art, where music is valued for its ability to be ignored rather than app
reciated, and that this has significant implications for the future of music and
creativity. With the rise of AI-generated music and the increasing importance o
f data-driven decision making in the music industry, the article asks what the m
usic we're not hearing sounds like, and what the consequences of this shift will
be for artists and listeners alike. The article concludes by highlighting the n
eed for a more nuanced understanding of the music industry and the impact of str
eaming on artists and listeners, and for alternative models that prioritize crea
tivity and autonomy over profit and convenience.Ejemplo: Generación de un Resumen Especificando Petición de Datos de Usuario, Máximo de Palabras y Estilo de Resumen
El siguiente ejemplo muestra cómo generar un resumen de un texto de más de 12000 palabras especificando los siguientes parámetros:
user_prompt: el resumen debe empezar por 'El resumen del artículo es: 'max_words: 100summary_style: list
SELECT DBMS_CLOUD_AI.SUMMARIZE(
location_uri => 'https://objectstorage.ca-toronto-1.' ||
'oraclecloud.com/n/namespace-string/b/' ||
'/bucketname/o/data_folder/' ||
'summary/dreams.txt',
credential_name => 'STORE_CRED',
profile_name => 'GENAI',
user_prompt => 'The summary should start with ''The summary of ' ||
'the article is: ''',
params => '{"max_words":100, "summary_style":"list"}')
As response FROM dual;
RESPONSE
--------------------------------------------------------------------------------
The summary of the article is:
- The book "Dreams" by Henri Bergson explores the concept of dreams and their si
gnificance in understanding human consciousness.
- Bergson argues that dreams are not just random thoughts, but rather a way for
our unconscious mind to process and consolidate memories.
- He suggests that dreams are a result of the relaxation of our mental faculties
, which allows our unconscious mind to freely associate and create new connectio
ns between memories.
- The book also discusses the role of sensations, such as visual and auditory im
pressions, in shaping our dreams.
- Bergson's theory of dreams is compared to other theories, including those of F
reud and Jung, and is seen as a unique and insightful contribution to the field
of psychology.
- The book concludes by highlighting the importance of studying dreams in order
to gain a deeper understanding of human consciousness and the workings of the mi
nd.Ejemplo: generación de un resumen de un libro
En este ejemplo, se muestra la transferencia de un archivo MiB 35.66 como entrada para generar un resumen. La función DBMS_CLOUD_AI.SUMMARIZE utiliza un método de acotación iterativo para procesar los fragmentos. Consulte Refinamiento iterativo para obtener más información.
SELECT DBMS_CLOUD_AI.SUMMARIZE(
location_uri => 'https://objectstorage.ca-toronto-1.oraclecloud.com/n/namespace-string/b/' ||
'bucketname/o/data_folder/summary/Descartes_An_Intellectual_Biography.pdf',
credential_name => 'STORE_CRED',
profile_name => 'GENAI',
params => '{"chunk_processing_method":"iterative_refinement"}')
AS response FROM dual;
RESPONSE
--------------------------------------------------------------------------------
Stephen Gaukroger's intellectual biography of Rene Descartes provides a detailed
examination of the philosopher's crucial role in shaping modern thought, placin
g him within the cultural, religious, and scientific context of the early sevent
eenth century. It traces Descartes' intellectual journey from his education at L
a Fleche, where he rejected Aristotelian logic, to his influential interactions
with figures like Isaac Beeckman, which shaped his mechanistic worldview evident
in works like his hydrostatics manuscript and *Compendium Musicae*. The biograp
hy underscores Descartes' dual commitment to philosophy and science, highlightin
g his social status among the gentry, mathematical innovations such as solving t
he Pappus problem through algebraic geometry, and his epistemology based on clea
r and distinct ideas. It explores his mechanistic explanations of bodily functio
ns, challenging traditional soul-body distinctions, and his extensive natural ph
ilosophy in texts like *Le Monde* and *L'Homme*. Gaukroger also delves into Desc
artes' cosmological theories, including the vortex theory and laws of motion lin
ked to divine immutability, as well as his nuanced perspectives on animal cognit
ion versus human consciousness. Central to the narrative is Descartes' use of hy
perbolic doubt to combat skepticism and establish metaphysical foundations throu
gh the *cogito*, alongside his classification of ideas and theological proofs of
God's existence. The complex relationship between his natural philosophy and me
taphysics, especially in defining motion as a mode, and his innovative approach
to the passions in *Passions of the Soul*, rejecting Stoic views for a mind-body
union, are key themes. This portrayal captures Descartes' struggle with traditi
onal paradigms during a transformative era, emphasizing his enduring impact on p
hilosophy and science.Tema principal: Ejemplos de uso de Select AI
Ejemplo: Seleccionar traducción de IA
En estos ejemplos se muestra cómo puede utilizar la capacidad translate.
Antes de empezar
Revisión:
Ejemplo: Uso de la Acción Translate en la Línea de Comandos SQL
En el siguiente ejemplo se muestra el uso de la acción translate en la línea de comandos SQL.
Nota
El perfil de AI debe especificar el idioma de destino. Esta función se admite únicamente para el proveedor OCI.
El perfil de AI debe especificar el idioma de destino. Esta función se admite únicamente para el proveedor OCI.
--Create an AI profile with language parameters
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GENAI_NEW',
attributes =>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"target_language": "french",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
SQL> exec DBMS_CLOUD_AI.SET_PROFILE('GENAI_NEW');
PL/SQL procedure successfully completed.
SQL> select ai translate I need to translate this;
RESPONSE
---------------------
Je dois traduire ceciEjemplo: uso de Traducir en la función DBMS_CLOUD_AI.GENERATE
Los siguientes ejemplos muestran el uso de translate como una acción Select AI dentro de la función DBMS_CLOUD_AI.GENERATE. Consulte GENERATE Function para obtener más información.
Nota
El perfil AI puede omitir la especificación del parámetro de idioma de destino si se transfiere como atributo en
El perfil AI puede omitir la especificación del parámetro de idioma de destino si se transfiere como atributo en
DBMS_CLOUD_AI.GENERATE.
La acción translate se proporciona en la función DBMS_CLOUD_AI.GENERATE junto con target_language y source_language. En este ejemplo, se utiliza la traducción de IA generativa. El texto de entrada this is a document en inglés (source_language: "en") se traduce al francés (target_language: "fr").
SELECT DBMS_CLOUD_AI.GENERATE('select ai translate text to be translated')
FROM dual;
DECLARE
l_attributes clob := '{"target_language": "fr", "source_language": "en"}';
output clob;
BEGIN
output := DBMS_CLOUD_AI.GENERATE(
prompt => 'this is a document',
profile_name => 'oci_translate',
action => 'translate',
attributes => l_attributes
);
Ejemplo: uso de la función DBMS_CLOUD_AI.TRANSLATE para la traducción
En este ejemplo, se llama a la función DBMS_CLOUD_AI.TRANSLATE para utilizar la traducción de IA generativa y se convierte el texto de entrada del inglés (source_language) al francés (target_language) mediante el perfil de IA especificado.
Consulte Función TRANSLATE para obtener más información.
BEGIN
output_text := DBMS_CLOUD_AI.TRANSLATE(
profile_name => 'GENAI_NEW'
text => 'text to be translated',
source_language => 'English',
target_language => 'French');
END;
/Ejemplo: visualización de idiomas admitidos para un proveedor
Consulte la vista AI_TRANSLATION_LANGUAGES para ver una lista de los idiomas que soporta el proveedor de IA. Consulte AI_TRANSLATION_LANGUAGES View para obtener más información.
SELECT* FROM AI_TRANSLATION_LANGUAGES;
LANGUAGE_NAME LANGUAGE_CODE PROVIDER
-------------------- -------------------- ---------------
ARABIC ar OCI
CROATIAN hr OCI
CZECH cs OCI
DANISH da OCI
GERMAN de OCI
GREEK el OCI
ENGLISH en OCI
SPANISH es OCI
FINNISH fi OCI
FRENCH fr OCI
FRENCH CANADA fr-CA OCI
HEBREW he OCI
HUNGARIAN hu OCI
ITALIAN it OCIEjemplo: Uso de un Perfil Diferente para el Lenguaje Natural para la Generación SQL
En este ejemplo se muestra la configuración de un perfil diferente denominado
openai con OpenAI como proveedor. Puede utilizar este perfil para generar SQL entre otras funciones y acciones de Select AI de sus peticiones de datos.
Nota
El perfil de este ejemplo no soporta la capacidad
El perfil de este ejemplo no soporta la capacidad
translate.
-- OpenAI profile used for sql generation
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name = > 'openai',
attributes => '{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner": "ADB_USER", "name": "GENRE"},
{"owner": "ADB_USER", "name": "CUSTOMER"},
{"owner": "ADB_USER", "name": "PIZZA_SHOP"},
{"owner": "ADB_USER", "name": "STREAMS"},
{"owner": "ADB_USER", "name": "MOVIES"},
{"owner": "ADB_USER", "name": "ACTIONS"}]
}');
END;
/
EXEC DBMS_CLOUD_AI.SET_PROFILE('openai');
SELECT AI SHOWSQL Give me the total number of customers;
RESPONSE
------------
SELECT COUNT("Customer_ID") AS "Total_Customers" FROM "Customers"Tema principal: Ejemplos de uso de Select AI
Ejemplo: restricción del acceso a tablas en el perfil de IA
En este ejemplo, se muestra cómo restringir el acceso a la tabla e indicar al LLM que utilice solo las tablas especificadas en el object_list del perfil de AI.
Defina enforce_object_list en true para restringir el acceso a la tabla al LLM.
Como usuario de base de datos, cree y configure su perfil de AI. Revise Realizar requisitos previos para Select AI para configurar su perfil de AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE_ENFORCED',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "GENRE"},
{"owner": "ADB_USER", "name": "CUSTOMER"},
{"owner": "ADB_USER", "name": "PIZZA_SHOP"},
{"owner": "ADB_USER", "name": "STREAMS"},
{"owner": "ADB_USER", "name": "MOVIES"},
{"owner": "ADB_USER", "name": "ACTIONS"}],
"enforce_object_list" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.set_profile('GOOGLE_ENFORCED');
PL/SQL procedure successfully completed.
select ai showsql please list the user tables;
RESPONSE
--------------------------------------------------------------------------------------------
SELECT 'ADB_USER.GENRE' AS TABLE_NAME FROM DUAL UNION ALL SELECT 'ADB_USER.CUSTOMER' AS
TABLE_NAME FROM DUAL UNION ALL SELECT 'ADB_USER.PIZZA_SHOP' AS TABLE_NAME FROM DUAL UNION
ALL SELECT 'ADB_USER.STREAMS' AS TABLE_NAME FROM DUAL UNION ALL SELECT 'ADB_USER.MOVIES'
AS TABLE_NAME FROM DUAL
--Al definir enforce_object_list en false, se indica al LLM que utilice otras tablas y vistas según sus conocimientos previos.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE_ENFORCED1',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "GENRE"},
{"owner": "ADB_USER", "name": "CUSTOMER"},
{"owner": "ADB_USER", "name": "PIZZA_SHOP"},
{"owner": "ADB_USER", "name": "STREAMS"},
{"owner": "ADB_USER", "name": "MOVIES"},
{"owner": "ADB_USER", "name": "ACTIONS"}],
"enforce_object_list" : "false"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.set_profile('GOOGLE_ENFORCED1');
PL/SQL procedure successfully completed.
select ai showsql please list the user tables;
RESPONSE
----------------------------------
SELECT TABLE_NAME FROM USER_TABLES
Tema principal: Ejemplos de uso de Select AI
Ejemplo: especificación de distinción entre mayúsculas y minúsculas para columnas
En este ejemplo, se muestra cómo puede definir la distinción entre mayúsculas y minúsculas para las columnas en el perfil AI.
Defina case_sensitive_values en false para recuperar consultas que no distinguen entre mayúsculas y minúsculas.
Como usuario de base de datos, cree y configure su perfil de AI. Revise Realizar requisitos previos para Select AI para configurar su perfil de AI.
BEGIN
DBMS_CLOUD_AI.create_profile(
profile_name =>'GOOGLE',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "GENRE"},
{"owner": "ADB_USER", "name": "CUSTOMER"},
{"owner": "ADB_USER", "name": "PIZZA_SHOP"},
{"owner": "ADB_USER", "name": "STREAMS"},
{"owner": "ADB_USER", "name": "MOVIES"},
{"owner": "ADB_USER", "name": "ACTIONS"}],
"case_sensitive_values" : "false"
}');
END;
/
PL/SQL procedure successfully completed.
-- With "case_sensitive_values" set to "false", LLM will give back case insensitive query.
select ai showsql how many people watch Inception;
RESPONSE
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c.CUSTOMER_ID) AS "COUNT"
FROM "ADB_USER"."CUSTOMER" c
JOIN "ADB_USER"."STREAMS" s ON c.CUSTOMER_ID = s.CUSTOMER_ID
JOIN "ADB_USER"."MOVIES" m ON s.MOVIE_ID = m.MOVIE_ID
WHERE UPPER(m.TITLE) = UPPER('Inception')Puede especificar una consulta sensible a mayúsculas/minúsculas mediante comillas dobles aunque case_sensitive_values esté definido en false.
select ai showsql how many people watch "Inception";
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c.CUSTOMER_ID) AS "COUNT"
FROM "ADB_USER"."CUSTOMER" c JOIN "ADB_USER"."STREAMS" s ON
c.CUSTOMER_ID = s.CUSTOMER_ID JOIN "ADB_USER"."MOVIES" m ON
s.MOVIE_ID = m.MOVIE_ID WHERE m.TITLE = 'Inception'
Tema principal: Ejemplos de uso de Select AI