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 illustrate common Select AI actions. - 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. - Example: Select AI with OpenAI
This example shows how you can use OpenAI to generate SQL statements from natural language prompts. - Example: Select AI with Cohere
This example shows how you can use Cohere to generate SQL statements from natural language prompts. - 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. - 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. - 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. - 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. - Example: Enable Conversations in Select AI
This example illustrates enabling conversations in Select AI. - 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. - 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. - Example: Generate Synthetic Data
This example explores how you can generate synthetic data mimicking the characteristics and distribution of real data.
Example: Select AI Actions
These examples illustrate common Select AI actions.
The following example illustrates actions such as
runsql (the default), 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.Parent topic: Examples of Using Select AI
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
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
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;
/Parent topic: Examples of Using Select AI
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
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
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'OPENAI_CRED',
username => 'OPENAI',
password => '<your_api_token>');
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OPENAI',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"OPENAI"
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
SQL> select ai 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.Parent topic: Examples of Using Select AI
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
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
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'COHERE_CRED',
username => 'COHERE',
password => 'your_api_token');
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Cohere endpoint
--
SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.cohere.ai',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'COHERE',
attributes =>'{"provider": "cohere",
"credential_name": "COHERE_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "sales"},
{"owner": "SH", "name": "products"},
{"owner": "SH", "name": "countries"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('COHERE');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"COHERE"
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('COHERE');
PL/SQL procedure successfully completed.Parent topic: Examples of Using Select AI
Example: Select AI with Azure OpenAI Service
The following examples shows how you can enable access to Azure OpenAI Service using your API key or use Azure OpenAI Service Principal, create an AI profile, and generate SQL from natural language prompts.
-- Create Credential for AI integration
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'AZURE_CRED',
username => 'AZUREAI',
password => 'your_api_token');
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for OpenAI endpoint
--SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => '<azure_resource_name>.openai.azure.com',
ace => xs$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
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: $48,677.18
SQL> select ai chat what is Autonomous Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous Database is a cloud-based database service provided by Oracle. It is
designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous Database uses machine learning
and automation to optimize performance, security, and availability, allowing us
ers to focus on their applications and data rather than database administration
tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous Data Warehouse (ADW) for analytical workloads. Autonomo
us Database provides high performance, scalability, and reliability, making it a
n ideal choice for modern cloud-based applications.
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.Parent topic: Examples of Using Select AI
Example: Select AI with Google
This example shows how you can
use Google to generate, run, and explain SQL from natural language prompts or chat using
the Google Gemini LLM.
The following example demonstrates using Google as your AI provider. The example demonstrates using your Google API signing key to provide network access, creating an AI profile, using Select AI actions to generate SQL queries from natural language prompts and chat responses.
--Grants EXECUTE privilege to ADB_USER
--
SQL> grant EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL> BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GOOGLE_CRED',
username => 'GOOGLE',
password => '<your_api_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Google endpoint
--
SQL>
SQL> BEGIN
DBMS_NETWORK_ACL_ADB_USER.APPEND_HOST_ACE(
host => 'generativelanguage.googleapis.com',
ace => xs$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.Parent topic: Examples of Using Select AI
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.
Parent topic: Examples of Using Select AI
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.Parent topic: Examples of Using Select AI
Example: Enable Conversations in Select AI
This example illustrates enabling conversations in Select AI.
Note
Only a DBA can run
Only a DBA can run
EXECUTE privileges and network ACL procedure.
Create your AI profile. Set the conversation attribute
to true in the profile, this action includes content from prior
interactions or prompts, potentially including schema metadata, and set your
profile. Once the profile is enabled, you can begin having conversations with your
data. Use natural language to ask questions and follow up as needed.
--Grants EXECUTE privilege to ADB_USER
--
SQL> grant execute on DBMS_CLOUD_AI to ADB_USER;
-- Grant Network ACL for OpenAI endpoint
--
SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.openai.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create Credential for AI provider
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'OPENAI_CRED',
username => 'OPENAI',
password => '<your_api_token>');
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OPENAI',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"OPENAI"
--
-- Use AI
--
what are the total number of customers;
CUSTOMER_COUNT
--------------
55500
break out count of customers by country;
RESPONSE
-----------------
COUNTRY_NAME CUSTOMER_COUNT
Italy 7780
Brazil 832
Japan 624
United Kingdom 7557
Germany 8173
United States of America 18520
France 3833
Canada 2010
Spain 2039
China 712
Singapore 597
New Zealand 244
Poland 708
Australia 831
Argentina 403
Denmark 383
South Africa 88
Saudi Arabia 75
Turkey 91
what age group is most common;
RESPONSE
--------------------------------------------------------------------------------
AGE_GROUP CUSTOMER_COUNT
65+ 28226
select ai keep the top 5 customers and their country by their purchases and include a rank in the result;
RESPONSE
--------------------------------------------------------------------------------
RANK CUSTOMER_NAME COUNTRY PURCHASES
1 Abigail Ruddy Japan 276
2 Abigail Ruddy Italy 168
3 Abigail Ruddy Japan 74
3 Abner Robbinette Germany 74
5 Abner Everett France 68
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('OPENAI');
PL/SQL procedure successfully completed.Parent topic: Examples of Using Select AI
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)
Parent topic: Examples of Using Select AI
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 DEVICEParent topic: Examples of Using Select AI
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
----------------
10Example: 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.
Parent topic: Examples of Using Select AI