Examples of Using Select AI

Explore integrating Oracle's Select AI with various supported AI providers to generate, run, and explain SQL from natural language prompts or chat with the LLM.

Example: Select AI Actions

These examples showcase common Select AI actions and guide you through setting up your profile with different AI providers to leverage those actions.

The following example illustrates actions such as runsql, showsql, narrate, chat, and explainsql that you can perform with SELECT AI. These examples use the sh schema with AI provider and profile attributes set in the DBMS_CLOUD_AI.CREATE_PROFILE function.

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.

Example: Select AI with OCI Generative AI

These examples show how you can access OCI Generative AI using your OCI API key or Resource Principal, create an AI profile, and generate, run, and explain SQL from natural language prompts or chat using the OCI Generative AI LLMs.

Note

OCI Generative AI uses meta.llama-3-70b-instruct as the default model if you do not specify the model_name. To learn more about the parameters, see Profile Attributes.
-- 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 Database;

RESPONSE                                                                                                                                                                                                                                                                                                    
An Autonomous 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 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 Databases include:
1. **Increased efficiency**: By automating routine tasks, DBAs can focus on more strategic activities.
2. **Improved performance**: Autonomous 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 Databases can help reduce labor costs and improve resource utilization.
5. **Improved reliability**: Autonomous Databases can detect and repair errors more quickly, reducing downtime and improving overall system reliability.
Oracle Autonomous Database is a popular example of an Autonomous Database, which was introduced in 2018. Other vendors, such as Amazon, Microsoft, and Google, also offer Autonomous Database services as part of their cloud offerings.
In summary, Autonomous 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.

--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('GENAI');
  
PL/SQL procedure successfully completed.

Example: Select AI with OCI Generative AI Resource Principal

To use resource principal with OCI Generative AI, Oracle Cloud Infrastructure tenancy administrator must grant access for Generative AI resources to a dynamic group. See Perform Prerequisites to Use Resource Principal with Autonomous Database to provide access to a dynamic group.

Set the required policies to obtain access to all Generative AI resources. See Getting Access to Generative AI to know more about Generative AI policies.
  • To get access to all Generative AI resources in the entire tenancy, use the following policy:

    allow group <your-group-name> to manage generative-ai-family in tenancy
  • To get access to all Generative AI resources in your compartment, use the following policy:

    allow group <your-group-name> to manage generative-ai-family in compartment <your-compartment-name>

Connect as an administrator and enable OCI resource principal. See ENABLE_PRINCIPAL_AUTH Procedure to configure the parameters.

Note

OCI Generative AI uses meta.llama-3-70b-instruct as the default model if you do not specify the model. To learn more about the parameters, see Profile Attributes.
-- 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 Database;

RESPONSE                                                                                                                                                                                                                                                                                                    
An Autonomous 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 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 Databases include:
1. **Increased efficiency**: By automating routine tasks, DBAs can focus on more strategic activities.
2. **Improved performance**: Autonomous 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 Databases can help reduce labor costs and improve resource utilization.
5. **Improved reliability**: Autonomous Databases can detect and repair errors more quickly, reducing downtime and improving overall system reliability.
Oracle Autonomous Database is a popular example of an Autonomous Database, which was introduced in 2018. Other vendors, such as Amazon, Microsoft, and Google, also offer Autonomous Database services as part of their cloud offerings.
In summary, Autonomous 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.


--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('GENAI');
  
PL/SQL procedure successfully completed.
Example: Select AI with OCI Generative AI Using LLAMA Model

This example showcases the chat feature of the LLAMA model from OCI Generative AI. It highlights the model's capabilities through two prompts: generating a friendly email to customers for movie recommendations and generate an introductory paragraph on rock climbing.

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": "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": "ACTORS"}
            ]
			"model": "meta.llama-2-70b-chat",
			"oci_runtimetype":"LLAMA", 
            "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 Generate a friendly email to customer Gilbert Lee with two action-thriller movie recommendations available through our MovieStream service;
SQL>  
	RESPONSE
	------------------------------------------------------------------------------------------------------------------------------------------------------
	.
	Subject: Action-packed movie recommendations for you!
	Dear Gilbert,
	I hope this email finds you well! I wanted to reach out to you today to recommend two action-thriller movies that are currently available on our Movie
	Stream service. I think you'll really enjoy them!
	The first movie I recommend is "John Wick" starring Keanu Reeves. This movie follows the story of a retired hitman who seeks vengeance against a power
	ful crime lord and his army of assassins. The action scenes are intense and non-stop, and Keanu Reeves delivers an outstanding performance.
	RESPONSE
	------------------------------------------------------------------------------------------------------------------------------------------------------
	The second movie I recommend is "Mission: Impossible - Fallout" starring Tom Cruise. This movie follows Ethan Hunt and his team as they try to prevent
	 a global catastrophe. The action scenes are heart-stopping and the stunts are truly impressive. Tom Cruise once again proves why he's one of the grea
	test action stars of all time.
	Both of these movies are sure to keep you on the edge of your seat and provide plenty of thrills and excitement. They're available to stream now on Mo
	vieStream, so be sure to check them out!
	If you have any questions or need assistance with MovieStream, please don't hesitate to reach out to me. I'm always here to help.
	Thank you for being a valued customer, and I hope you enjoy the movies!
	RESPONSE
	------------------------------------------------------------------------------------------------------------------------------------------------------
	Best regards,
	[Your Name]
	MovieStream Customer Service
	
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, an
	d 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 flex
	ibility, 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!
Using OCI Generative AI with the Default Model

The following example uses the default OCI Generative AI Chat Model, meta.llama-3-70b-instruct. OCI Generative AI uses meta.llama-3-70b-instruct as the default model if you do not specify the model.

BEGIN                                                                        
  DBMS_CLOUD_AI.CREATE_PROFILE(                                              
      profile_name => 'OCI_DEFAULT',
      attributes   => '{"provider": "oci",
                        "credential_name": "OCI_CRED",
                        "object_list": [{"owner": "ADB_USER"}]
                        }');
END;                                                                         
/
Using OCI Generative AI with Chat Model

The following example uses cohere.command-r-plus as the OCI Generative AI Chat Model.

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"
                       }');
END;
/
Using OCI Generative AI with Chat Model Endpoint ID

The following example demonstrates how to specify the OCI Generative AI Chat Model endpoint ID instead of the model.

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;
/
Using OCI Generative AI with Chat Model OCID

This example demonstrates how to specify the OCI Generative AI Chat Model OCID as the model.

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;
/
Using OCI Generative AI with Generation Model

This example demonstrates how to specify the OCI Generative AI Generation Model such as cohere.command as the model.

BEGIN                                                                           
  DBMS_CLOUD_AI.CREATE_PROFILE(
      profile_name => 'OCI_COHERE_COMMAND',
      attributes   => '{"provider": "oci",
                        "credential_name": "OCI_CRED",
                        "object_list": [{"owner": "ADB_USER"}],
                        "model": "cohere.command"
                       }');
END;
/ 
Using OCI Generative AI with Generation Model Endpoint ID

This example demonstrates how to use the OCI Generative AI Generation Model endpoint ID instead of the model.

BEGIN                                                                           
  DBMS_CLOUD_AI.CREATE_PROFILE(
      profile_name => 'OCI_GENTEXT_ENDPOINT',
      attributes   => '{"provider": "oci",
                        "credential_name": "OCI_CRED",
                        "object_list": [{"owner": "ADB_USER"}],
                        "oci_endpoint_id": "<endpoint_id>"
                        "oci_runtimetype": "COHERE"
                       }');
END;
/
Using OCI Generative AI with Generation Model OCID

This example demonstrates how to specify the OCI Generative AI Generation Model OCID as the model.

BEGIN                                                                           
  DBMS_CLOUD_AI.CREATE_PROFILE(
      profile_name => 'OCI_GENTEXT_OCID',
      attributes   => '{"provider": "oci",
                        "credential_name": "OCI_CRED",
                        "object_list": [{"owner": "ADB_USER"}],
                        "model": "<model_ocid>"
                        "oci_runtimetype": "LLAMA"
                       }');
END;
/

Example: Select AI with OpenAI

This example shows how you can use OpenAI to generate SQL statements from natural language prompts.

Note

Only a DBA can run EXECUTE privileges and network ACL procedure.

--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
--
SQL> EXEC DBMS_CLOUD.CREATE_CREDENTIAL('OPENAI_CRED', 'OPENAI', '<your api token>');
 
PL/SQL procedure successfully completed.
 
--
-- Create AI profile
--SQL> BEGIN                                                                        
     DBMS_CLOUD_AI.CREATE_PROFILE(                                              
      'OPENAI',                                                             
      '{"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 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 Database;
 
RESPONSE
--------------------------------------------------------------------------------
Autonomous 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 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 Data Warehouse (ADW) for analytical workloads. Autonomo
us Database provides high performance, scalability, and reliability, making it a
n ideal choice for modern cloud-based applications.


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> EXEC DBMS_CLOUD_AI.DROP_PROFILE('OPENAI');
 
PL/SQL procedure successfully completed.

Example: Select AI with Cohere

This example shows how you can use Cohere to generate SQL statements from natural language prompts.

Note

Only a DBA can run EXECUTE privileges and network ACL procedure.

--Grants EXECUTE privilege to ADB_USER
--
SQL>grant execute on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL> EXEC DBMS_CLOUD.CREATE_CREDENTIAL('COHERE_CRED', 'COHERE', '<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
--SQL> BEGIN
  DBMS_CLOUD_AI.CREATE_PROFILE(
      'COHERE',
      '{"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
 
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('COHERE');
 
PL/SQL procedure successfully completed.

Example: Select AI with Azure OpenAI Service

The following examples shows how you can enable access to Azure OpenAI Service using your API key or use Azure OpenAI Service Principal, create an AI profile, and generate SQL from natural language prompts.

-- Create Credential for AI integration
--
SQL> EXEC DBMS_CLOUD.CREATE_CREDENTIAL('AZURE_CRED', 'AZUREAI', '<your api token>');
  
PL/SQL procedure successfully completed.
  
--
-- Grant Network ACL for 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
--
SQL> 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
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('AZUREAI');
  
PL/SQL procedure successfully completed.

--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
 
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"AZUREAI"
  
  
--
-- 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 Database;
  
RESPONSE
--------------------------------------------------------------------------------
Autonomous 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 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 Data Warehouse (ADW) for analytical workloads. Autonomo
us Database provides high performance, scalability, and reliability, making it a
n ideal choice for modern cloud-based applications.


SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('AZUREAI');
  
PL/SQL procedure successfully completed.
Example: Select AI with Azure OpenAI Service Principal

Connect as a database administrator to provide access to Azure service principal authentication and then grant the network ACL permissions to the user (ADB_USER) who wants to use Select AI. To provide access to Azure resources, see Use Azure Service Principal to Access Azure Resources.

Note

Only a DBA user can run EXECUTE privileges and network ACL procedure.
-- 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
--SQL> 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
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('AZUREAI');
  
PL/SQL procedure successfully completed.
  
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
 
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"AZUREAI"  

--
-- 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 Database;
  
RESPONSE
--------------------------------------------------------------------------------
Autonomous 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 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 Data Warehouse (ADW) for analytical workloads. Autonomo
us Database provides high performance, scalability, and reliability, making it a
n ideal choice for modern cloud-based applications.
 
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('AZUREAI');
  
PL/SQL procedure successfully completed.

Example: Select AI with Google

This example shows how you can use Google to generate, run, and explain SQL from natural language prompts or chat using the Google Gemini LLM.

The following example demonstrates using Google as your AI provider. The example demonstrates using your Google API signing key to provide network access, creating an AI profile, using Select AI actions to generate SQL queries from natural language prompts and chat responses.

--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": "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"}
                   ]
         }');
     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 list the year that most of the movies are released;
 
RELEASE_YEAR
------------
        2020
 
1 row selected.                                                               
                                                                                         
SQL> select ai showsql List movies watched by users but not in genre 'Comedy';         
                                                                                         
RESPONSE                                                                               
--------------------------------------------------------------------------------       
SELECT DISTINCT m.TITLE AS MOVIE_TITLE FROM "ADMIN"."MOVIES" m JOIN "ADMIN"."WAT       
CH_HISTORY" wh ON m.MOVIE_ID = wh.MOVIE_ID JOIN "ADMIN"."MOVIE_GENRES" mg ON m.M       
OVIE_ID = mg.MOVIE_ID JOIN "ADMIN"."GENRES" g ON mg.GENRE_ID = g.GENRE_ID WHERE        
g.GENRE_NAME != 'Comedy'                                                               
                                                                                         
                                                                                         
1 row selected.

SQL> select ai showsql Show users who have watched at least one movie from each genre;
 
RESPONSE
--------------------------------------------------------------------------------
SELECT DISTINCT "ADMIN"."USERS"."USER_NAME" FROM "ADMIN"."USERS" JOIN "ADMIN"."W
ATCH_HISTORY" ON "ADMIN"."USERS"."USER_ID" = "ADMIN"."WATCH_HISTORY"."USER_ID" J
OIN "ADMIN"."MOVIES" ON "ADMIN"."WATCH_HISTORY"."MOVIE_ID" = "ADMIN"."MOVIES"."M
OVIE_ID" JOIN "ADMIN"."MOVIE_GENRES" ON "ADMIN"."MOVIES"."MOVIE_ID" = "ADMIN"."M
OVIE_GENRES"."MOVIE_ID" JOIN "ADMIN"."GENRES" ON "ADMIN"."MOVIE_GENRES"."GENRE_I
D" = "ADMIN"."GENRES"."GENRE_ID" GROUP BY "ADMIN"."USERS"."USER_NAME" HAVING COU
NT(DISTINCT "ADMIN"."GENRES"."GENRE_ID") = (SELECT COUNT(*) FROM "ADMIN"."GENRES
")
 
 
1 row selected.                                                                         
                                                                                         
SQL> select ai explainsql the top 3 most popular genres based on movie watch counts;   
                                                                                         
RESPONSE                                                                               
--------------------------------------------------------------------------------       
```sql                                                                                 
SELECT                                                                                 
    g."GENRE_NAME" AS "Genre Name",                                                    
    COUNT(DISTINCT wh."WATCH_ID") AS "Watch Count"                                     
FROM                                                                                   
    "ADMIN"."GENRES" g                                                                 
JOIN                                                                                   
    "ADMIN"."MOVIE_GENRES" mg ON g."GENRE_ID" = mg."GENRE_ID"                          
JOIN                                                                                   
    "ADMIN"."MOVIES" m ON mg."MOVIE_ID" = m."MOVIE_ID"                                 
JOIN                                                                                   
    "ADMIN"."WATCH_HISTORY" wh ON m."MOVIE_ID" = wh."MOVIE_ID"                         
GROUP BY                                                                               
    g."GENRE_NAME"                                                                     
ORDER BY                                                                               
    "Watch Count" DESC                                                                 
FETCH FIRST 3 ROWS ONLY;                                                               
```    
                                                                                         
**Explanation:**                                                                       
 
1. **FROM and JOIN Clauses:**
   - We start by selecting data from the "GENRES" table (aliased as "g") to get
the genre names.
   - We join it with "MOVIE_GENRES" (aliased as "mg") on the common column "GENR
E_ID" to link genres to movies.
   - We join "MOVIE_GENRES" with "MOVIES" (aliased as "m") using "MOVIE_ID" to c
onnect movies to their genres.
   - Finally, we join "MOVIES" with "WATCH_HISTORY" (aliased as "wh") using "MOV
IE_ID" to connect movies to watch history records.
 
2. **SELECT Clause:**
   - `g."GENRE_NAME" AS "Genre Name"`: Selects the genre name from the "GENRES"
table and aliases it as "Genre Name".
   - `COUNT(DISTINCT wh."WATCH_ID") AS "Watch Count"`: Counts the distinct watch
 IDs to get the number of times movies in each genre have been watched. We use D
ISTINCT to avoid counting the same watch record multiple times if a movie belong
s to multiple genres.
 
3. **GROUP BY Clause:**
   - We group the results by "Genre Name" to count watch counts for each distinc
t genre.
 
4. **ORDER BY Clause:**
   - `ORDER BY "Watch Count" DESC`: Sorts the result set in descending order bas
ed on the "Watch Count" column to get the most popular genres first.
 
5. **FETCH FIRST 3 ROWS ONLY:**
   - This clause limits the result set to the top 3 rows, effectively returning
only the top 3 most popular genres.
 
**Key Points:**
 
- **Case Sensitivity:** Oracle SQL is case-sensitive. We use double quotes to en
close table and column names to preserve their original casing.
- **Table and Column Aliases:** Using aliases makes the query more readable and
concise.
- **DISTINCT:** Ensures accurate watch count by eliminating duplicates from the
watch history records.
- **ORDER BY and FETCH FIRST:** These clauses effectively retrieve the top 3 mos
t popular genres.
 
**Key Points:**
 
- **Case Sensitivity:** Oracle SQL is case-sensitive. We use double quotes to en
close table and column names to preserve their original casing.
- **Table and Column Aliases:** Using aliases makes the query more readable and
concise.
- **DISTINCT:** Ensures accurate watch count by eliminating duplicates from the
watch history records.
- **ORDER BY and FETCH FIRST:** These clauses effectively retrieve the top 3 mos
t popular genres.
 
 
1 row selected.
 
SQL> select ai narrate Show the average and total watch counts per genre;
 
RESPONSE
--------------------------------------------------------------------------------
The answer shows the total and average number of times movies belonging to each
genre were watched.
For example:
- Action genre movies were watched 3 times in total and the average watch count
for Action genre movies is 3.
- Comedy genre movies were watched 3 times in total and the average watch count       
for Comedy genre movies is 3.
- Drama genre movies were watched 2 times in total and the average watch count f      
or Drama genre movies is 2.
 
 
1 row selected. 


SQL> select ai chat how long the history of the movie industry is;                    
 
RESPONSE
--------------------------------------------------------------------------------
The history of the movie industry is long and complex, spanning over **130 years
**.
 
Here's a quick timeline to give you an idea:
 
* **1890s:** The first motion pictures were created, leading to the development
of early film cameras and projectors.
* **1900s:** The first movie theaters opened, and early film studios began to em
erge.
* **1910s:** The rise of Hollywood as a center for film production, and the deve
lopment of narrative storytelling in film.
* **1920s:** The introduction of sound to film, marking a significant shift in t
he industry.
* **1930s:** The Golden Age of Hollywood, with the rise of iconic stars and stud
ios.
* **1940s:** The industry grapples with World War II and the rise of television.
 
* **1950s:** The introduction of color film and wide-screen formats.
* **1960s:** The rise of independent cinema and the New Hollywood era.
* **1970s:** The rise of blockbuster movies and the influence of Hollywood on gl
obal culture.
* **1980s:** The rise of home video and the increasing influence of technology i
n filmmaking.
* **1990s:** The digital revolution in film and the emergence of new distributio
n platforms.
* **2000s:** The rise of streaming services and the continued impact of technolo
gy on the industry.
 
The movie industry has gone through many changes throughout its history, but its
 impact on culture and entertainment remains undeniable.
 
 
1 row selected.

Example: Select AI with Anthropic

This example shows how you can use Anthropic to generate, run, and explain SQL from natural language prompts or chat using the Anthropic Claude LLM.

The following example demonstrates using Anthropic as your AI provider. The example demonstrates using your Anthropic API signing key to provide network access, creating an AI profile, and using Select AI actions to generate SQL queries from natural language prompts and chat using the Anthropic Claude LLM.

See Profile Attributes to supply the profile attributes.

--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": "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"}
                   ]
         }');
    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 list all genres;
 
  GENRE_ID GENRE_NAME
---------- --------------------------------------------------
         1 Action
         2 Comedy
         3 Drama
 
3 rows selected.
 
SQL>SELECT AI showsql List the movie titles and their release dates for movies that hav
e a duration greater than 120 minutes;                                                 
                                                                                         
RESPONSE                                                                               
--------------------------------------------------------------------------------       
SELECT m."TITLE" AS movie_title, m."RELEASE_DATE" AS release_date                      
FROM "ADB_USER"."MOVIES" m                                                                
WHERE m."DURATION" > 120                                                               
ORDER BY m."RELEASE_DATE"                                                              
                                                                                         
                                                                                         
1 row selected.            

SQL>SELECT AI showsql List movies watched by users but not in genre 'Comedy';
 
RESPONSE
--------------------------------------------------------------------------------
SELECT DISTINCT m."TITLE" AS movie_title, u."USER_NAME" AS viewer
FROM "ADB_USER"."MOVIES" m
JOIN "ADB_USER"."WATCH_HISTORY" wh ON m."MOVIE_ID" = wh."MOVIE_ID"
JOIN "ADB_USER"."USERS" u ON wh."USER_ID" = u."USER_ID"
WHERE m."MOVIE_ID" NOT IN (
    SELECT mg."MOVIE_ID"
    FROM "ADB_USER"."MOVIE_GENRES" mg
    JOIN "ADB_USER"."GENRES" g ON mg."GENRE_ID" = g."GENRE_ID"
    WHERE g."GENRE_NAME" = 'Comedy'
)
ORDER BY m."TITLE", u."USER_NAME"
                                                         
                                                                                         
SQL>SELECT AI explainsql Find the unique user names and the count of movies they have w
atched;                                                                                
                                                                                         
RESPONSE                                                                               
--------------------------------------------------------------------------------       
Here's the Oracle SQL query to find the unique user names and the count of movie       
s they have watched, along with a detailed explanation:                                
                                                                                         
```sql                                                                                 
SELECT                                                                                 
    u."USER_NAME",                                                                     
    COUNT(DISTINCT wh."MOVIE_ID") AS movies_watched_count                              
FROM                                                                                   
    "ADB_USER"."USERS" u                                                                  
LEFT JOIN                                                                              
    "ADB_USER"."WATCH_HISTORY" wh ON u."USER_ID" = wh."USER_ID"                           
GROUP BY                                                                               
    u."USER_NAME"                                                                      
ORDER BY                                                                               
    movies_watched_count DESC;
```
 
Explanation:
 
1. FROM clause:
   - We start with the "ADB_USER"."USERS" table and alias it as 'u'.
 
2. LEFT JOIN:
   - We use a LEFT JOIN with the "ADB_USER"."WATCH_HISTORY" table (aliased as 'wh')
 to include all users, even those who haven't watched any movies.
   - The join condition is u."USER_ID" = wh."USER_ID", linking users to their wa
tch history.
 
3. SELECT clause:
   - We select u."USER_NAME" to get the unique user names.
   - We use COUNT(DISTINCT wh."MOVIE_ID") to count the number of unique movies w
atched by each user. This is aliased as 'movies_watched_count'.
   - The DISTINCT keyword ensures that if a user has watched the same movie mult
iple times, it's only counted once.
 
4. GROUP BY clause:
   - We group the results by u."USER_NAME" to get one row per unique user.
 
5. ORDER BY clause:
   - We order the results by movies_watched_count in descending order (DESC) to
show users who have watched the most movies first.
 
This query will return a list of all users and the number of unique movies they
have watched, including users who haven't watched any movies (they will have a c
ount of 0 or NULL). The results will be sorted with the most active users (those
 who have watched the most movies) at the top.
 
 
1 row selected.
 
SQL>SELECT AI narrate Show the average and total watch counts per genre;             
 
RESPONSE
--------------------------------------------------------------------------------      
This result shows the watch statistics for different movie genres:                    
 
- Action and Comedy genres are the most popular, each with 3 total watches and a      
n average of 3 watches per movie.
- Drama genre follows with 2 total watches and an average of 2 watches per movie      
.
 
These numbers indicate how often movies in each genre are being watched by users      
, giving insight into genre popularity and viewing trends.                            
 
 
1 row selected.
 
SQL>SELECT AI chat which movie is the most famous;
 
RESPONSE
--------------------------------------------------------------------------------
It's difficult to definitively say which movie is the most famous, as popularity
 and fame can be measured in different ways and can vary across cultures and tim
e periods. However, some movies that are widely considered to be among the most
famous globally include:
 
1. "Gone with the Wind" (1939)
2. "Star Wars: Episode IV - A New Hope" (1977)
3. "Titanic" (1997)
4. "The Godfather" (1972)
5. "E.T. the Extra-Terrestrial" (1982)
6. "Jaws" (1975)
7. "Avatar" (2009)
8. "The Wizard of Oz" (1939)
9. "Casablanca" (1942)
10. "Jurassic Park" (1993)
 
These films are renowned for their cultural impact, box office success, critical 
acclaim, and enduring popularity. However, the concept of "most famous" can be
subjective and may change over time as new films are released and gain recogniti
on. 

--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('ANTHROPIC');
  
PL/SQL procedure successfully completed.

Example: Select AI with Hugging Face

This example shows how you can use Hugging Face to generate, run, and explain SQL from natural language prompts or chat using the Hugging Face LLM.

The following example demonstrates using Hugging Face as your AI provider. The example demonstrates using your Hugging Face API signing key to provide network access, creating an AI profile, and using Select AI actions to generate SQL queries from natural language prompts and chat using the Hugging Face LLM.

--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": "ADB_USER", "name": "users"},
                  {"owner": "ADB_USER", "name": "moviesdata"}],
           "model" : "mistralai/Mixtral-8x7B-Instruct-v0.1"
         }');
    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 list all genres;

  GENRE_ID GENRE_NAME                                        
---------- --------------------------------------------------
         1 Action                                            
         2 Comedy                                            
         3 Drama        

                                    
SQL>SELECT AI showsql List movies watched by users but not in genre 'Comedy';

SELECT DISTINCT m.TITLE
FROM "ADMIN"."MOVIE_SALES_FACT" m
WHERE m.GENRE NOT IN ('Comedy')
)   
                                                                     
                                                                                         
SQL>SELECT AI explainsql Find the unique user names and the count of movies they have watched;   

RESPONSE                                                                                                                                                                                                                                                                           
--------------------------------------------------------------------------------------------
Sure, here's an example of how you can find the unique user names and the count of movies they have watched using Oracle SQL and table aliases:
```sql
SELECT u.user_name, COUNT(DISTINCT m.movie_id) as movie_count
FROM ADMIN.USERS u
JOIN ADMIN.WATCH_HISTORY wh ON u.user_id = wh.user_id
JOIN ADMIN.MOVIES m ON wh.movie_id = m.movie_id
GROUP BY u.user_name;
```
Explanation:

1. We start by selecting the user name (`u.user_name`) and the count of distinct movie IDs (`COUNT(DISTINCT m.movie_id)`) as `movie_count` from the `USERS` table (aliased as `u`).
2. We then join the `USERS` table with the `WATCH_HISTORY` table (aliased as `wh`) on the `user_id` column. This gives us a dataset of all the movies watched by each user.
3. We further join the resulting dataset with the `MOVIES` table (aliased as `m`) on the `movie_id` column. This allows us to get the actual movie details for each movie ID.
4. Finally, we group the resulting dataset by the `user_name` column and apply the `COUNT` function to get the count of unique movies watched by each user.

Note that we have used table aliases (`u`, `wh`, and `m`) to make the query easier to read and write. We have also enclosed the schema name (`ADMIN`) and column names (`user_name`, `movie_id`) in double quotes to ensure that they are treated as case-sensitive.



To find the unique user names and the count of movies they have watched, you can use the following Oracle SQL query:
```vbnet
SELECT 
  u.USERNAME, 
  COUNT(m.ORDER_NUM) AS MOVIE_COUNT
FROM 
  ADMIN.MOVIE_SALES_FACT m
JOIN 
  ADMIN.CUSTOMER c ON m.CUSTOMER_ID = c.CUSTOMER_ID
JOIN 
  ADMIN.USER u ON c.USERNAME = u.USERNAME
GROUP BY 
  u.USERNAME;
```
Explanation:

1. We start by selecting the `USERNAME` column from the `ADMIN.USER` table and the count of movies watched by each user, which we calculate using the `COUNT` function and the `ORDER_NUM` column from the `ADMIN.MOVIE_SALES_FACT` table.
2. We then join the `ADMIN.MOVIE_SALES_FACT` table with the `ADMIN.CUSTOMER` table on the `CUSTOMER_ID` column, which allows us to associate each movie sale with a specific customer.
3. Next, we join the `ADMIN.CUSTOMER` table with the `ADMIN.USER` table on the `USERNAME` column, which allows us to associate each customer with a specific user.
4. Finally, we group the results by `USERNAME` using the `GROUP BY` clause, which allows us to count the number of movies watched by each user.

Using table aliases (`m`, `c`, and `u`) makes the query easier to read and write, especially when dealing with large and complex table structures. Additionally, enclosing table and column names in double quotes ensures that they are case sensitive, which is important in Oracle SQL.


SQL>SELECT AI narrate Show the average and total watch counts per genre;
 
The result provides the average and total watch counts per movie genre. For each genre, there is one record with two properties: "AVERAGE\_WATCH\_COUNT" and "TOTAL\_WATCH\_COUNT". The "AVERAGE\_WATCH\_COUNT" represents the average number of times a movie in that genre has been watched, while the "TOTAL\_WATCH\_COUNT" indicates the total number of times all movies in that genre have been watched.

Here are the genres and their corresponding average and total watch counts:

* Family: Average watch count is 1, with a total of 5,681,078 watches
* Mystery: Average watch count is 1, with a total of 833,989 watches
* Documentary: Average watch count is 1, with a total of 59,297 watches
* Adventure: Average watch count is 1, with a total of 13,414,954 watches
* Sport: Average watch count is 1, with a total of 931,251 watches
* News: Average watch count is 1, with a total of 32,090 watches
* Fantasy: Average watch count is 1, with a total of 5,115,105 watches
* Film-Noir: Average watch count is 1, with a total of 2,830,396 watches
* Romance: Average watch count is 1, with a total of 4,046,663 watches
* Horror: Average watch count is 1, with a total of 4,560,312 watches
* Thriller: Average watch count is 1, with a total of 6,395,926 watches
* History: Average watch count is 1, with a total of 1,182,406 watches
* Biography: Average watch count is 1, with a total of 1,191,253 watches
* Reality-TV: Average watch count is 1, with a total of 346,821 watches
* Comedy: Average watch count is 1, with a total of 11,645,917 watches
* Drama: Average watch count is 1, with a total of 18,577,932 watches
* Sci-Fi: Average watch count is 1, with a total of 7,885,454 watches
* Crime: Average watch count is 1, with a total of 5,406,563 watches
* Musical: Average watch count is 1, with a total of 2,935,625 watches
* War: Average watch count is 1, with a total of 4,237,470 watches

Note that the average watch count is 1 for all genres, meaning each movie in these genres has been watched at least once.

SQL>SELECT AI chat which movie is the most famous;                                       
                                                                                         
It's difficult to determine the most "famous" movie of all time, as fame can be subjective and depends on cultural, demographic, and personal factors. However, some of the most critically acclaimed and commercially successful movies in history include:

* The Godfather (1972)
* The Shawshank Redemption (1994)
* Pulp Fiction (1994)
* The Dark Knight (2008)
* Titanic (1997)
* Star Wars (1977)
* The Lord of the Rings: The Return of the King (2003)
* Inception (2010)
* Schindler's List (1993)
* Casablanca (1942)

These movies have had a significant impact on the film industry and have been widely recognized for their storytelling, direction, acting, and cultural significance.

Example: Set Up and Use Select AI with RAG

This example guides you through setting up credentials, configuring network access, and creating a vector index for integrating OCI Generative AI vector store cloud services with OpenAI using Oracle Autonomous Database.

The setup concludes with creating an AI profile that uses the vector index to enhance LLM responses. Finally, this example uses the Select AI narrate action, which returns a response that has been enhanced using information from the specified vector database.

The following example demonstrates building and querying vector index in Oracle 23ai.

SQL> -- Create the OpenAI credential
SQL> BEGIN
  2    DBMS_CLOUD.CREATE_CREDENTIAL(
  3      credential_name => 'OPENAI_CRED',
  4      username => 'OPENAI_CRED',
  5      password => '<your_api_key>'
  6    );
  7  END;
  8  /

PL/SQL procedure successfully completed.

SQL> 
SQL> -- Append the OpenAI endpoint
SQL> BEGIN
  2      DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
  3           host => 'api.openai.com',
  4           ace  => xs$ace_type(privilege_list => xs$name_list('http'),
  5                   principal_name => 'ADMIN',
  6                   principal_type => xs_acl.ptype_db)
  7     );
  8  END;
  9  /

PL/SQL procedure successfully completed.

 SQL> 
SQL> -- Create the object store credential
SQL> BEGIN
  2    DBMS_CLOUD.CREATE_CREDENTIAL(
  3      credential_name => 'OCI_CRED',
  4      username => '<your_username>',
  5      password => '<OCI_profile_password>'
  6    );
  7  END;
  8  /

PL/SQL procedure successfully completed.

SQL> -- Create the profile with the vector index.
SQL> BEGIN
  2    DBMS_CLOUD_AI.CREATE_PROFILE(
  3        profile_name =>'OPENAI_ORACLE',
  4        attributes   =>'{"provider": "openai",
  5          "credential_name": "OPENAI_CRED",
  6          "vector_index_name": "MY_INDEX",
  7          "temperature": 0.2,
  8          "max_tokens": 4096,
  9          "model": "gpt-3.5-turbo-1106"
 10         }');
 11  end;
 12  /

PL/SQL procedure successfully completed.

 
SQL> -- Set profile
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI_ORACLE');

PL/SQL procedure successfully completed.                                            
 
SQL> -- create a vector index with the vector store name, object store location and
SQL> -- object store credential
SQL> 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.  
                                                                                
SQL> -- After the vector index is populated, we can now query the index.




SQL> -- Set profile
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI_ORACLE');

PL/SQL procedure successfully completed.

SQL> -- Select AI answers the question with the knowledge available in the vector database.
SQL> set pages 1000
SQL> set linesize 150
SQL> 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)
  - 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)

Example: Improve SQL Query Generation with Table and Column Comments

This example demonstrates how comments in database tables and columns can improve the generation of SQL queries from natural language prompts.

In this example, Azure OpenAI Service acts as the AI provider. The "comments":"true" parameter in DBMS_CLOUD_AI.CREATE_PROFILE function determines whether comments are passed to the model for SQL generation.
-- 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 DEVICE

Example: Generate Synthetic Data

This example explores how you can generate synthetic data mimicking the characteristics and distribution of real data.

The following example shows how to create a few tables in your schema, use OCI Generative AI as your AI provider to create an AI profile, synthesize data into those tables using the DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA function, and query or generate responses to natural language prompts with 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
Example: Generate Synthetic Data for Multiple Tables

After you create and set your AI provider profile, use the DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA to generate data for multiple tables. You can query or use Select AI to respond to the natural language prompts.

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
----------------
              10
Example: Guide Synthetic Data Generation with Sample Rows

To guide AI service in generating synthetic data, you can randomly select existing records from a table. For instance, by adding {"sample_rows": 5} to the params argument, you can send 5 sample rows from a table to the AI provider. This example generates 10 additional rows based on the sample rows from the Transactions table.

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;
/
Example: Customize Synthetic Data Generation with User Prompts

The user_prompt argument enables you to specify additional rules or requirements for data generation. This can be applied to a single table or as part of the object_list argument for multiple tables. For example, in the following calls to DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA, the prompt instructs the AI to generate synthetic data on movies released in 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;
/
Example: Improve Synthetic Data Quality by Using Table Statistics

If a table has column statistics or is cloned from a database that includes metadata, Select AI can use these statistics to generate data that closely resembles or is consistent with the original data.

For NUMBER columns, the high and low values from the statistics guide the value range. For instance, if the SALARY column in the original EMPLOYEES table ranges from 1000 to 10000, the synthetic data for this column will also fall within this range.

For columns with distinct values, such as a STATE column with values CA, WA, and TX, the synthetic data will use these specific values. You can manage this feature using the {"table_statistics": true/false} parameter. By default, the table statistics are enabled.

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;
/
Example: Use Column Comments to Guide Data Generation

If column comments exist, Select AI automatically includes them to provide additional information for the LLM during data generation. For example, a comment on the Status column in a Transaction table might list allowed values such as successful, failed, pending, canceled, and need manual check. You can also add comments to further explain the column, giving AI services more precise instructions or hints for generating accurate data. By default, comments are disabled. See Optional Parameters for more details.

-- 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;
/
Example: Set Unique Values in Synthetic Data Generation

When generating large amounts of synthetic data with LLMs, duplicate values are likely to occur. To prevent this, set up a unique constraint on the relevant column. This ensures that Select AI ignores rows with duplicate values in the LLM response. Additionally, to restrict values for certain columns, you can use the user_prompt or add comments to specify the allowed values, such as limiting a STATE column to CA, WA, and 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'
/
Example: Enhance Synthetic Data Generation by Parallel Processing

To reduce runtime, Select AI splits synthetic data generation tasks into smaller chunks for tables without primary keys or with numeric primary keys. These tasks run in parallel, interacting with the AI provider to generate data more efficiently. The Degree of Parallelism (DOP) in your database, influenced by your Autonomous Database service level and ECPU or OCPU settings, determines the number of records each chunk processes. Running tasks in parallel generally improves performance, especially when generating large amounts of data across many tables. To manage the parallel processing of synthetic data generation, set priority as an optional parameter. See Optional Parameters.