Select AIの使用例
OracleのSelect AIをサポートされている様々なAIプロバイダと統合して、自然言語プロンプトからSQLを生成、実行および説明したり、LLMとチャットしたりする方法をご紹介します。
- 例: AIアクションの選択
次の例は、一般的なAI選択アクションを示しています。 - 例: OCI生成AIを使用したAIの選択
これらの例は、OCI APIキーまたはリソース・プリンシパルを使用してOCI生成AIにアクセスし、AIプロファイルを作成し、OCI生成AI LLMを使用して自然言語プロンプトまたはチャットからSQLを生成、実行および説明する方法を示しています。 - 例: OpenAIを使用したAIの選択
この例では、OpenAIを使用して自然言語プロンプトからSQL文を生成する方法を示します。 - 例: Cohereを使用したAIの選択
この例は、Cohereを使用して自然言語プロンプトからSQL文を生成する方法を示しています。 - 例: Azure OpenAIサービスを使用したAIの選択
次の例は、APIキーを使用してAzure OpenAIサービスへのアクセスを有効にする方法、またはAzure OpenAIサービス・プリンシパルを使用したり、AIプロファイルを作成したり、自然言語プロンプトからSQLを生成する方法を示しています。 - 例: Googleを使用したAIの選択
この例は、Googleを使用して、Google Gemini LLMを使用して自然言語プロンプトまたはチャットからSQLを生成、実行および説明する方法を示しています。 - 例: Anthropicを使用したAIの選択
この例は、Anthropic Claude LLMを使用して自然言語プロンプトまたはチャットからSQLを生成、実行および説明する方法を示しています。 - 例: 顔を抱くAIの選択
この例は、Hugging Faceを使用して、自然言語プロンプトまたはHugging Face LLMを使用してチャットからSQLを生成、実行および説明する方法を示しています。 - 例: 選択AIでの会話の有効化
この例は、選択AIでの会話の有効化を示しています。 - 例: RAGを使用したSelect AIの設定および使用
この例では、資格証明の設定、ネットワーク・アクセスの構成、およびOracle Autonomous Databaseを使用してOCI生成AIベクトル・ストア・クラウド・サービスをOpenAIと統合するためのベクトル索引の作成について説明します。 - 例: データベース内トランスフォーマ・モデルを使用したAIの選択
この例では、Oracleオブジェクト・ストレージに格納されている事前トレーニング済トランスフォーマ・モデルをOracle Database 23aiインスタンスにインポートし、Select AIプロファイルのインポート済データベース内モデルを使用してドキュメント・チャンクおよびユーザー・プロンプトのベクトル埋込みを生成する方法を示します。 - 例: SQL問合せ生成の改善
これらの例は、データベース表および列のコメント、注釈、外部キーおよび参照整合性制約によって、自然言語プロンプトからのSQL問合せの生成を改善する方法を示しています。 - 例: 合成データの生成
この例では、実データの特性と分布を模倣した合成データを生成する方法を探ります。 - 例: データ・アクセスの有効化または無効化
この例は、管理者がデータ・アクセスを制御し、Select AIが実際のスキーマ表をLLMに送信しないようにする方法を示しています。 - 例: AIプロファイルでの表アクセスの制限
この例では、表アクセスを制限し、AIプロファイルのobject_listで指定された表のみを使用するようにLLMに指示する方法を示します。 - 例: 列の大文字と小文字の区別の指定
この例は、AIプロファイルの列の大文字と小文字の区別を設定する方法を示しています。
親トピック: Select AIを使用したデータベースとの自然言語対話
例: AIアクションの選択
これらの例は、一般的な「AIの選択」アクションを示しています。
次の例は、SELECT AIで実行できるrunsql (デフォルト)、showsql、narrate、chat、explainsqlなどのアクションを示しています。これらの例では、DBMS_CLOUD_AI.CREATE_PROFILE関数で設定されたAIプロバイダおよびプロファイル属性でshスキーマを使用します。
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.親トピック: Select AIの使用例
例: OCI生成AIを使用したAIの選択
これらの例は、OCI APIキーまたはリソース・プリンシパルを使用してOCI生成AIにアクセスし、AIプロファイルを作成し、OCI生成AI LLMを使用して自然言語プロンプトまたはチャットからSQLを生成、実行および説明する方法を示しています。
ノート
model_nameパラメータを指定しない場合、OCI生成AIは、AIプロバイダおよびLLMの選択の表に従ってデフォルト・モデルを使用します。パラメータについてさらに学習するには、プロファイル属性を参照してください。
-- 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.例: OCI生成AIリソース・プリンシパルを使用したAIの選択
OCI生成AIでリソース・プリンシパルを使用するには、Oracle Cloud Infrastructureテナンシ管理者が生成AIリソースのアクセス権を動的グループに付与する必要があります。動的グループへのアクセスを提供するには、Autonomous Databaseでリソース・プリンシパルを使用するための前提条件の実行を参照してください。
すべての生成AIリソースへのアクセスを取得するために必要なポリシーを設定します。生成AIポリシーの詳細は、生成AIへのアクセスを参照してください。
-
テナンシ全体のすべての生成AIリソースにアクセスするには、次のポリシーを使用します:
allow group <your-group-name> to manage generative-ai-family in tenancy -
コンパートメント内のすべての生成AIリソースにアクセスするには、次のポリシーを使用します:
allow group <your-group-name> to manage generative-ai-family in compartment <your-compartment-name>
管理者として接続し、OCIリソース・プリンシパルを有効にします。パラメータを構成するには、ENABLE_PRINCIPAL_AUTHプロシージャを参照してください。
ノート
modelを指定しない場合、OCI生成AIではmeta.llama-3-70b-instructがデフォルト・モデルとして使用されます。パラメータの詳細は、「プロファイル属性」を参照してください。
-- 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.例: OCI生成AIプロファイルの別のリージョンの指定
この例では、プロファイルでOCI生成AIでサポートされているリージョンを指定する方法を示します。生成AIを使用するリージョンを参照してください。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'GENAI',
attributes =>'{"provider": "oci",
"object_list": [
{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}
]
"region":"eu-frankfurt-1",
"model": "meta.llama-3.3-70b-instruct",
"credential_name": "GENAI_CRED",
"oci_compartment_id": "ocid1.compartment.oc1..."}');
END;
/例: LLAMAモデルを使用したOCI生成AIを使用したAIの選択
この例では、OCI生成AIのchat機能を示します。2つのプロンプト(顧客のコメントを分析してセンチメントを把握し、ロッククライミングの概要段落を生成する)によって、モデルの機能が強調されます。
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaa',
tenancy_ocid => 'ocid1.tenancy.oc1..aaa',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
PL/SQL procedure successfully completed.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'GENAI',
attributes =>'{"provider": "oci",
"object_list": [
{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}
]
"model": "meta.llama-3.3-70b-instruct",
"oci_apiformat":"GENERIC",
"credential_name": "GENAI_CRED",
"oci_compartment_id": "ocid1.compartment.oc1..."}');
END;
/
PL/SQL procedure successfully completed.
--
--Set profile
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('GENAI');
PL/SQL procedure successfully completed.
SQL> set linesize 150
SQL> SELECT AI chat what is the sentiment of this comment I am not going to waste my time filling up this three page form. Lousy idea;
SQL>
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------
The sentiment of this comment is strongly negative. The user is expressing frustration and annoyance with the idea of filling out a three-page form, an
d is explicitly stating that they consider it a "lousy idea". The use of the phrase "waste my time" also implies that they feel the task is unnecessary
and unproductive. The tone is dismissive and critical.
SQL> SELECT AI chat Write an enthusiastic introductory paragraph on how to get started with rock climbing with Athletes as the target audience;
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------
Rock climbing is an exhilarating and challenging sport that's perfect for athletes looking to push their limits and test their strength, endurance,
and mental toughness. Whether you're a seasoned athlete or just starting out, rock climbing offers a unique and rewarding experience that will have
you hooked from the very first climb. With its combination of physical and mental challenges, rock climbing is a great way to build strength, improve
flexibility, and develop problem-solving skills. Plus, with the supportive community of climbers and the breathtaking views from the top of the climb,
you'll be hooked from the very first climb. So, if you're ready to take on a new challenge and experience the thrill of adventure, then it's time to
get started with rock climbing!デフォルト・モデルでのOCI生成AIの使用
次の例では、デフォルトのOCI生成AIチャット・モデルを使用します。model_nameパラメータを指定しない場合、OCI生成AIは、「AIプロバイダおよびLLMの選択」の表に従ってデフォルト・モデルを使用します。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_DEFAULT',
attributes => '{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [{"owner": "ADB_USER"}]
}');
END;
/チャット・モデルでのOCI生成AIの使用
次の例では、OCI生成AIチャット・モデルとしてcohere.command-r-plus-08-2024を使用します。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_COHERE_COMMAND_R_PLUS',
attributes => '{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [{"owner": "ADB_USER"}],
"model": "cohere.command-r-plus-08-2024"
}');
END;
/チャット・モデル・エンドポイントIDでのOCI生成AIの使用
次の例では、modelではなくOCI生成AIチャット・モデルのエンドポイントIDを指定する方法を示します。メタLlamaチャット・モデルのエンドポイントIDを使用している場合は、oci_apiformatをGENERICとして指定します。
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;
/チャット・モデルOCIDでのOCI生成AIの使用
この例では、modelではなくOCI生成AI Cohereチャット・モデルのエンドポイントIDを指定する方法を示します。メタLlamaチャット・モデルのエンドポイントIDを使用している場合は、oci_apiformatをGENERICとして指定します。
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;
/親トピック: Select AIの使用例
例: OpenAIを使用したAIの選択
この例では、OpenAIを使用して自然言語プロンプトからSQL文を生成する方法を示します。
ノート
EXECUTE権限およびネットワークACLプロシージャを実行できるのは、DBAのみです。
--Grants EXECUTE privilege to ADB_USER
--
SQL> grant execute on DBMS_CLOUD_AI to ADB_USER;
-- Grant Network ACL for OpenAI endpoint
--
SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.openai.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create Credential for AI provider
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'OPENAI_CRED',
username => 'OPENAI',
password => '<your_api_token>');
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OPENAI',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"OPENAI"
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
SQL> select ai chat what is Autonomous Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous Database uses machine learning and automation to optimize performance, security, and availability, allowing
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for
transactional workloads and Autonomous Data Warehouse (ADW) for analytical workloads. Autonomous Database provides high performance, scalability, and
reliability, making it an ideal choice for modern cloud-based applications.
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('OPENAI');
PL/SQL procedure successfully completed.次の例は、AIプロファイルで別のモデルを指定する方法を示しています。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OPENAI',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"model": "gpt-3.5-turbo",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/ 親トピック: Select AIの使用例
例: CohereでAIを選択
この例では、Cohereを使用して自然言語プロンプトからSQL文を生成する方法を示します。
ノート
EXECUTE権限およびネットワークACLプロシージャを実行できるのは、DBAのみです。
--Grants EXECUTE privilege to ADB_USER
--
SQL>GRANT execute on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'COHERE_CRED',
username => 'COHERE',
password => 'your_api_token');
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Cohere endpoint
--
SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.cohere.ai',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'COHERE',
attributes =>'{"provider": "cohere",
"credential_name": "COHERE_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "sales"},
{"owner": "SH", "name": "products"},
{"owner": "SH", "name": "countries"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('COHERE');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"COHERE"
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('COHERE');
PL/SQL procedure successfully completed.次の例は、AIプロファイルで異なるモデル属性とカスタム属性を指定する方法を示しています。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'COHERE',
attributes =>
'{"provider": "cohere",
"credential_name": "COHERE_CRED",
"model": "cohere.command-a-03-2025",
"object_list": [{"owner": "ADB_USER"}],
"max_tokens":512,
"stop_tokens": [";"],
"temperature": 0.5,
"comments": true
}');
END;
/親トピック: Select AIの使用例
例: Azure OpenAI Serviceを使用したAIの選択
次の例では、APIキーを使用してAzure OpenAIサービスへのアクセスを有効にするか、Azure OpenAIサービス・プリンシパルを使用してAIプロファイルを作成し、自然言語プロンプトからSQLを生成する方法を示します。
-- Create Credential for AI integration
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'AZURE_CRED',
username => 'AZUREAI',
password => 'your_api_token');
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for OpenAI endpoint
--
BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => '<azure_resource_name>.openai.azure.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=> 'AZUREAI',
attributes=> '{"provider": "azure",
"azure_resource_name": "<azure_resource_name>",
"azure_deployment_name": "<azure_deployment_name>"
"credential_name": "AZURE_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"AZUREAI"
--
-- Use AI
--
select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
select ai chat what is Autonomous Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous Database uses machine learning and automation to optimize performance, security, and availability, allowing
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous Data Warehouse (ADW) for analytical workloads. Autonomous Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
EXEC DBMS_CLOUD_AI.DROP_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.次の例は、AIプロファイルで別のモデルを指定する方法を示しています。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'AZUREAI',
attributes=>'{"provider": "azure",
"credential_name": "AZURE$PA",
"model": "gpt-3.5-turbo",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"azure_resource_name": "<azure_resource_name>",
"azure_deployment_name": "<azure_deployment_name>"
}');
END;
/例: Azure OpenAIサービス・プリンシパルを使用したAIの選択
データベース管理者として接続してAzureサービス・プリンシパル認証へのアクセスを提供し、Select AIを使用するユーザー(ADB_USER)にネットワークACL権限を付与します。Azureリソースへのアクセスを提供するには、Azureサービス・プリンシパルを使用したAzureリソースへのアクセスを参照してください。
ノート
EXECUTE権限およびネットワークACLプロシージャを実行できるのは、DBAユーザーのみです。
-- Connect as ADMIN user and enable Azure service principal authentication.
BEGIN
DBMS_CLOUD_ADMIN.ENABLE_PRINCIPAL_AUTH(provider => 'AZURE',
params => JSON_OBJECT('azure_tenantid' value 'azure_tenantid'));
END;
/
-- Copy the consent url from cloud_integrations view and consents the ADB-S application.
SQL> select param_value from CLOUD_INTEGRATIONS where param_name = 'azure_consent_url';
PARAM_VALUE
--------------------------------------------------------------------------------
https://login.microsoftonline.com/<tenant_id>/oauth2/v2.0/authorize?client_id=<client_id>&response_type=code&scope=User.read
-- On the Azure OpenAI IAM console, search for the Azure application name and assign the permission to the application.
-- You can get the application name in the cloud_integrations view.
SQL> select param_value from CLOUD_INTEGRATIONS where param_name = 'azure_app_name';
PARAM_VALUE
--------------------------------------------------------------------------------
ADBS_APP_DATABASE_OCID
--
-- Grant Network ACL for Azure OpenAI endpoint
--SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'azure_resource_name.openai.azure.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'AZUREAI',
attributes=>'{"provider": "azure",
"credential_name": "AZURE$PA",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"azure_resource_name": "<azure_resource_name>",
"azure_deployment_name": "<azure_deployment_name>"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"AZUREAI"
--
-- Use AI
--
select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
select ai chat what is Autonomous Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous Database uses machine learning and automation to optimize performance, security, and availability, allowing us
ers to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous Data Warehouse (ADW) for analytical workloads. Autonomous Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
EXEC DBMS_CLOUD_AI.DROP_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.親トピック: Select AIの使用例
例: GoogleでAIを選択
この例では、Googleを使用して、Google Gemini LLMを使用して自然言語プロンプトまたはチャットからSQLを生成、実行および説明する方法を示します。
次の例は、GoogleをAIプロバイダとして使用する方法を示しています。この例では、Google API署名キーを使用してネットワーク・アクセスを提供し、AIプロファイルを作成し、Select AIアクションを使用して自然言語プロンプトおよびチャット・レスポンスからSQL問合せを生成する方法を示します。
--Grants EXECUTE privilege to ADB_USER
--
SQL> grant EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL> BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GOOGLE_CRED',
username => 'GOOGLE',
password => '<your_api_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Google endpoint
--
SQL>
SQL> BEGIN
DBMS_NETWORK_ACL_ADB_USER.APPEND_HOST_ACE(
host => 'generativelanguage.googleapis.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
SQL> BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('GOOGLE');
PL/SQL procedure successfully completed.
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
SQL> select ai chat what is Autonomous 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
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous Data Warehouse (ADW) for analytical workloads. Autonomous Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('GOOGLE');
PL/SQL procedure successfully completed. 次の例は、AIプロファイルで別のモデルを指定する方法を示しています。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"model": "gemini-1.5-pro",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/親トピック: Select AIの使用例
例: 人類学によるAIの選択
この例では、Anthropicを使用して、Anthropic Claude LLMを使用して自然言語プロンプトまたはチャットからSQLを生成、実行および説明する方法を示します。
次の例は、AIプロバイダとしてのAnthropicの使用を示しています。この例では、Anthropic API署名キーを使用してネットワーク・アクセスを提供し、AIプロファイルを作成し、Select AIアクションを使用して、Anthropic Claude LLMを使用して自然言語プロンプトおよびチャットからSQL問合せを生成する方法を示します。
プロファイル属性を指定するには、「プロファイル属性」を参照してください。
--Grant EXECUTE privilege to ADB_USER
SQL>GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL>BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'ANTHROPIC_CRED',
username => 'ANTHROPIC',
password => '<your api key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Anthropic endpoint
--
SQL>BEGIN
DBMS_NETWORK_ACL_ADB_USER.APPEND_HOST_ACE(
host => 'api.anthropic.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
SQL>BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'ANTHROPIC',
attributes =>'{"provider": "anthropic",
"credential_name": "ANTHROPIC_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL>EXEC DBMS_CLOUD_AI.SET_PROFILE('ANTHROPIC');
PL/SQL procedure successfully completed.
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
SQL> select ai chat what is Autonomous 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
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous Data Warehouse (ADW) for analytical workloads. Autonomous Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('ANTHROPIC');
PL/SQL procedure successfully completed.
次の例は、AIプロファイルで別のモデルを指定する方法を示しています。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'ANTHROPIC',
attributes =>'{"provider": "anthropic",
"credential_name": "ANTHROPIC_CRED",
"model": "claude-3-opus-20240229",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/親トピック: Select AIの使用例
例: Hugging Faceを使用したAIの選択
この例では、Hugging Faceを使用して、Hugging Face LLMを使用して自然言語プロンプトまたはチャットからSQLを生成、実行および説明する方法を示します。
次の例は、AIプロバイダとしてのHugging Faceの使用を示しています。この例では、Hugging Face API署名キーを使用してネットワーク・アクセスを提供し、AIプロファイルを作成し、Select AIアクションを使用して自然言語プロンプトからSQL問合せを生成し、Hugging Face LLMを使用してチャットする方法を示します。
--Grant EXECUTE privilege to ADB_USER
SQL>GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL>BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'HF_CRED',
username => 'HF',
password => '<your_api_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Hugging Face endpoint
--
SQL>BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api-inference.huggingface.co',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
SQL>BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'HF',
attributes =>'{"provider": "huggingface",
"credential_name": "HF_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"model" : "Qwen/Qwen2.5-72B-Instruct"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL>EXEC DBMS_CLOUD_AI.SET_PROFILE('HF');
PL/SQL procedure successfully completed.
--
-- Use AI
--SQL> select ai how many customers exist;
Customer_Count
--------------
55500
SQL> select ai how many customers in San Francisco are married;
Married_Customers
-----------------
46
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
------------------------------------------------------------
SELECT COUNT("CUST_ID") AS "Married_Customers"
FROM "SH"."CUSTOMERS" "C"
WHERE "CUST_CITY" = 'San Francisco' AND "CUST_MARITAL_STATUS
" = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
------------------------------------------------------------
To answer the question "How many customers in San Francisco are married?", we need to query the "SH"."CUSTOMERS" table and filter the results based on the city and marital status.
We will use table aliases to make the query more readable and ensure that the string comparisons follow the specified rules.
Here is the Oracle SQL query:
```sql
SELECT COUNT("CUST_ID") AS "Married_Customers_in_San_Francisco"
FROM "SH"."CUSTOMERS" c
WHERE c."CUST_CITY" = 'San Francisco' -- Case insensitive comparison
AND UPPER(c."CUST_MARITAL_STATUS") = UPPER('Married'); --
Case insensitive comparison
```
### Explanation:
1. **Table Alias**:
RESPONSE
------------------------------------------------------------
- We use the alias `c` for the "SH"."CUSTOMERS" table to make the query more readable.
2. **Column Selection**:
- We select the count of "CUST_ID" to get the number of customers who meet the criteria. We alias this count as "Married_Customers_in_San_Francisco" for clarity.
3. **WHERE Clause**:
- **City Filter**: `c."CUST_CITY" = 'San Francisco'`
- Since 'San Francisco' is not in double quotes, we use a case-insensitive comparison.
- **Marital Status Filter**: `UPPER(c."CUST_MARITAL_STATUS") = UPPER('Married')`
- Since 'Married' is not in double quotes, we use a case-insensitive comparison by applying the `UPPER` function to both sides of the comparison.
This query will return the number of customers in San Francisco who are married.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
------------------------------------------------------------
The top 3 customers in San Francisco are:
- Hector Colven, with a customer ID of 8866.
- Milburn Klemm, with a customer ID of 1944.
- Gavin Xie, with a customer ID of 2579.
These customers have the highest total sales among all customers in San Francisco.
SQL> select ai chat what is Autonomous Database;
RESPONSE
------------------------------------------------------------
An Autonomous Database is a type of database management system that uses artificial intelligence (AI) and machine learning (ML) to automate many of the routine tasks typically performed by database administrators (DBAs). These tasks include provisioning, patching, backups, tuning, and security. The goal of an autonomous database is to reduce the need for human intervention, minimize errors, and optimize performance,thereby allowing organizations to focus on more strategic activities.
### Key Features of Autonomous Databases:
RESPONSE
------------------------------------------------------------
1. **Automated Provisioning**: The database can be set up and configured automatically, reducing the time and effort required to get it up and running.
2. **Self-Patching and Updates**: The database automatically applies security patches and updates without downtime, ensuring that the system remains secure and up-to-date.
3. **Self-Tuning**: The database continuously monitors its performance and adjusts settings to optimize query execution and resource utilization.
4. **Self-Backup and Recovery**: Automated backup and recovery processes ensure that data is protected and can be restored quickly in the event of a failure.
5. **Security**: Advanced security features, including threat detection and response, are built into the database to protect against cyber threats.
6. **Scalability**: The database can automatically scale resources up or down based on demand, ensuring optimal performance and cost efficiency.
7. **Monitoring and Diagnostics**: Real-time monitoring and diagnostics help identify and resolve issues before they impact performance.
RESPONSE
------------------------------------------------------------
### Benefits of Autonomous Databases:
- **Reduced Operational Costs**: By automating routine tasks, the need for dedicated DBAs is reduced, lowering operational costs.
- **Improved Reliability**: Automated processes reduce the risk of human error, leading to more reliable and consistent performance.
- **Enhanced Security**: Continuous monitoring and automated security measures help protect against threats.
- **Faster Time to Market**: Automated provisioning and tuning allow applications to be deployed more quickly.
RESPONSE
------------------------------------------------------------
- **Scalability and Flexibility**: The ability to scale resources automatically ensures that the database can handle varying workloads efficiently.
### Use Cases:
- **Cloud Applications**: Autonomous databases are particularly useful in cloud environments where scalability and reliability are critical.
- **Data Warehousing**: They can handle large volumes of data and complex queries, making them ideal for data warehousing and analytics.
RESPONSE
------------------------------------------------------------
- **IoT and Real-Time Data Processing**: They can process and analyze real-time data from IoT devices efficiently.
- **E-commerce**: They can handle high transaction volumes and ensure fast response times for online shopping platforms.
### Examples of Autonomous Databases:
- **Oracle Autonomous Database**: One of the first and most well-known autonomous databases, offering both transactional
and data warehousing capabilities.
- **Amazon Aurora**: A managed relational database service that includes automated scaling, patching, and backups.
- **Microsoft Azure SQL Database Managed Instance**: Provides a high level of automation and management for SQL Server databases in the cloud.
- **Google Cloud Spanner**: A globally distributed, horizontally scalable relational database that is highly available and consistent.
Autonomous databases represent a significant advancement in database technology, offering organizations a more efficient, secure, and cost-effective way to manage their data.
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('HF');
PL/SQL procedure successfully completed.親トピック: Select AIの使用例
例: 選択AIでの会話の有効化
次の例では、Select AIでの会話の有効化を説明します。
ノート
EXECUTE権限およびネットワークACLプロシージャを実行できるのは、DBAのみです。
AIプロファイルを作成します。プロファイルでconversation属性をtrueに設定します。このアクションには、スキーマ・メタデータを含む可能性のある以前の相互作用またはプロンプトからのコンテンツが含まれ、プロファイルを設定します。プロファイルを有効にすると、データとの会話を開始できます。自然言語を使用して質問し、必要に応じてフォローアップします。
--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.親トピック: Select AIの使用例
例: RAGでのSelect AIの設定および使用
この例では、Oracle Autonomous Databaseを使用してOCI生成AIベクトル・ストア・クラウド・サービスをOpenAIと統合するための資格証明の設定、ネットワーク・アクセスの構成およびベクトル索引の作成を順を追って説明します。
この設定は、ベクトル索引を使用してLLMレスポンスを拡張するAIプロファイルの作成で終わります。最後に、この例では、指定されたベクトル・データベースの情報を使用して拡張されたレスポンスを返す「AIの選択」narrateアクションを使用します。
次の例は、Oracle 23aiでのベクトル索引の構築および問合せを示しています。
--Grants EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--Grants EXECUTE privilege DBMS_CLOUD_PIPELINE to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_PIPELINE to ADB_USER;
-- Create the OpenAI credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OPENAI_CRED',
username => 'OPENAI_CRED',
password => '<your_api_key>'
);
END;
/
PL/SQL procedure successfully completed.
-- Append the OpenAI endpoint
BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.openai.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
-- Create the object store credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OCI_CRED',
username => '<your_username>',
password => '<OCI_profile_password>'
);
END;
/
PL/SQL procedure successfully completed.
-- Create the profile with the vector index.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'OPENAI_ORACLE',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"vector_index_name": "MY_INDEX",
"temperature": 0.2,
"max_tokens": 4096,
"model": "gpt-3.5-turbo-1106"
}');
END;
/
PL/SQL procedure successfully completed.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI_ORACLE');
PL/SQL procedure successfully completed.
-- create a vector index with the vector store name, object store location and
-- object store credential
BEGIN
DBMS_CLOUD_AI.CREATE_VECTOR_INDEX(
index_name => 'MY_INDEX',
attributes => '{"vector_db_provider": "oracle",
"location": "https://swiftobjectstorage.us-phoenix-1.oraclecloud.com/v1/my_namespace/my_bucket/my_data_folder",
"object_storage_credential_name": "OCI_CRED",
"profile_name": "OPENAI_ORACLE",
"vector_dimension": 1536,
"vector_distance_metric": "cosine",
"chunk_overlap":128,
"chunk_size":1024
}');
END;
/
PL/SQL procedure successfully completed.
-- After the vector index is populated, we can now query the index.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI_ORACLE');
PL/SQL procedure successfully completed.
-- Select AI answers the question with the knowledge available in the vector database.
set pages 1000
set linesize 150
SELECT AI narrate how can I deploy an oracle machine learning model;
RESPONSE
To deploy an Oracle Machine Learning model, you would first build your model within the Oracle database. Once your in-database models are built, they become immediately available for use, for instance, through a SQL query using the prediction operators built into the SQL language.
The model scoring, like model building, occurs directly in the database, eliminating the need for a separate engine or environment within which the model and corresponding algorithm code operate. You can also use models from a different schema (user account) if the appropriate permissions are in place.
Sources:
- Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt (https://objectstorage.../v1/my_namespace/my_bucket/my_data_folder/Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt)
- Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-Database-Machine-Learning-and-APEX.txt (https://objectstorage.../v1/my_namespace/my_bucket/my_data_folder/Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-Database-Machine-Learning-and-APEX.txt)
親トピック: Select AIの使用例
例: データベース内トランスフォーマ・モデルを使用したAIの選択
この例では、Oracleオブジェクト・ストレージに格納されている事前トレーニング済トランスフォーマ・モデルをOracle Database 23aiインスタンスにインポートし、Select AIプロファイルのインポート済データベース内モデルを使用してドキュメント・チャンクおよびユーザー・プロンプトのベクトル埋込みを生成する方法を示します。
Select AIプロファイルでデータベース内トランスフォーマ・モデルを使用するには、次のものがあることを確認してください。
-
Oracle Database 23aiインスタンスにインポートされた事前トレーニング済モデル。
-
オプションで、Oracleオブジェクト・ストレージにアクセスします。
Oracle Object StorageからOracle Database 23aiへの事前トレーニング済トランスフォーマ・モデルのインポート
事前トレーニング済トランスフォーマ・モデルをデータベースにインポートするには、「データベース内のベクトル生成のONNX形式での事前トレーニング済モデルのインポート」のステップおよびブログのOracle Database 23aiの事前ビルド済埋込み生成モデルのステップを確認します。
次の例は、Oracleオブジェクト・ストレージからデータベースに保護トランスフォーマ・モデルをインポートし、インポートされたモデルを表示する方法を示しています。
- Create a Directory object, or use an existing directory object
CREATE OR REPLACE DIRECTORY ONNX_DIR AS 'onnx_model';
-- Object storage bucket
VAR location_uri VARCHAR2(4000);
EXEC :location_uri := 'https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/eLddQappgBJ7jNi6Guz9m9LOtYe2u8LWY19GfgU8flFK4N9YgP4kTlrE9Px3pE12/n/adwc4pm/b/OML-Resources/o/';
-- Model file name
VAR file_name VARCHAR2(512);
EXEC :file_name := 'all_MiniLM_L12_v2.onnx';
-- Download ONNX model from object storage into the directory object
BEGIN
DBMS_CLOUD.GET_OBJECT(
credential_name => NULL,
directory_name => 'ONNX_DIR',
object_uri => :location_uri || :file_name);
END;
/
-- Load the ONNX model into the database
BEGIN
DBMS_VECTOR.LOAD_ONNX_MODEL(
directory => 'ONNX_DIR',
file_name => :file_name,
model_name => 'MY_ONNX_MODEL');
END;
/
-- Verify
SELECT model_name, algorithm, mining_function
FROM user_mining_models
WHERE model_name='MY_ONNX_MODEL';選択AIプロファイルでのデータベース内トランスフォーマ・モデルの使用
これらの例は、Select AIプロファイル内でデータベース内トランスフォーマ・モデルを使用する方法を示しています。1つのプロファイルはベクトル埋込みを生成するためにのみ構成され、もう1つのプロファイルはSelect AIアクションとベクトル索引作成の両方をサポートしています。
「Select AIを実行するための権限の例」を確認して、前提条件を完了します。
次に、ベクトル埋め込みのみを生成する例を示します。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'EMBEDDING_PROFILE',
attributes => '{"provider" : "database",
"embedding_model": "MY_ONNX_MODEL"}'
);
END;
/次に、サポートされているAIプロバイダを指定できる一般的なSelect AIアクションとベクトルインデックス生成の例を示します。この例では、OCI Gen AIプロファイルおよび資格証明を使用します。サポートされているプロバイダのリストは、AIプロバイダおよびLLMの選択を参照してください。ただし、ベクトル埋込みの生成にデータベース内トランスフォーマ・モデルを使用する場合は、embedding_model属性で"database: <MY_ONNX_MODEL>"を使用します。
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaaa...',
tenancy_ocid => 'ocid1.tenancy.oc1..aaaa...',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"model": "meta.llama-3.3-70b-instruct",
"credential_name": "GENAI_CRED",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: MY_ONNX_MODEL"}'
);
END;
/別のスキーマからのデータベース内トランスフォーマ・モデルでのSelect AIの使用
この例では、別のスキーマ所有者がモデルを所有している場合、データベース内トランスフォーマ・モデルで「AIの選択」を使用する方法を示します。embedding_model属性で、モデルの完全修飾名としてschema_name.object_nameを指定します。現在のユーザーがスキーマ所有者またはモデルを所有している場合は、スキーマ名を省略できます。
別のスキーマ所有者がモデルを所有している場合は、次の権限があることを確認してください。
CREATE ANY MINING MODELシステム権限SELECT ANY MINING MODELシステム権限- 特定のモデルに対する
SELECT MINING MODELオブジェクト権限
システム権限を付与するには、ADMIN OPTIONを指定したシステム権限またはGRANT ANY PRIVILEGEシステム権限が付与されている必要があります。
権限を確認するには、Oracle Machine Learning for SQLのためのシステム権限を参照してください。
次の文は、ADB_USER1による任意のスキーマ内のデータのスコアリングおよびモデルの詳細の表示を許可します(SELECTアクセス権がデータに付与されている場合)。ただし、ADB_USER1では、ADB_USER1スキーマにのみモデルを作成できます。
GRANT CREATE MINING MODEL TO ADB_USER1;
GRANT SELECT ANY MINING MODEL TO ADB_USER1;
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"credential_name": "GENAI_CRED",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: ADB_USER1.MY_ONNX_MODEL"}'
);
END;
/次の例は、大/小文字を区別するモデル・オブジェクト名を指定する方法を示しています。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"credential_name": "GENAI_CRED",
"model": "meta.llama-3.3-70b-instruct",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: \"adb_user1\".\"my_model\""}'
);
END;
/様々なAIプロバイダーによるエンドツーエンドの例
これらの例は、Select AI RAGでデータベース内トランスフォーマ・モデルを使用するためのエンドツーエンドのステップを示しています。1つのプロファイルでは、埋込みベクトルの生成のために排他的に作成されたproviderとしてdatabaseを使用し、もう1つのプロファイルでは、Select AIアクションおよびベクトル索引用に作成されたproviderとしてociを使用します。
必要な権限を指定するには、Perform Prerequisites for Select AIを参照してください。
--Grant create any directory privilege to the user
GRANT CREATE ANY DIRECTORY to ADB_USER;
- Create a Directory object, or use an existing directory object
CREATE OR REPLACE DIRECTORY ONNX_DIR AS 'onnx_model';
-- Object storage bucket
VAR location_uri VARCHAR2(4000);
EXEC :location_uri := 'https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/eLddQappgBJ7jNi6Guz9m9LOtYe2u8LWY19GfgU8flFK4N9YgP4kTlrE9Px3pE12/n/adwc4pm/b/OML-Resources/o/';
-- Model file name
VAR file_name VARCHAR2(512);
EXEC :file_name := 'all_MiniLM_L12_v2.onnx';
-- Download ONNX model from object storage into the directory object
BEGIN
DBMS_CLOUD.GET_OBJECT(
credential_name => NULL,
directory_name => 'ONNX_DIR',
object_uri => :location_uri || :file_name);
END;
/
-- Load the ONNX model into the database
BEGIN
DBMS_VECTOR.LOAD_ONNX_MODEL(
directory => 'ONNX_DIR',
file_name => :file_name,
model_name => 'MY_ONNX_MODEL');
END;
/
-- Verify
SELECT model_name, algorithm, mining_function
FROM user_mining_models
WHERE model_name='MY_ONNX_MODEL';
--Administrator grants EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--Administrator grants EXECUTE privilege DBMS_CLOUD_PIPELINE to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_PIPELINE to ADB_USER;
-- Create the object store credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OCI_CRED',
username => '<your_username>',
password => '<OCI_profile_password>'
);
END;
/
PL/SQL procedure successfully completed.
-- Create the profile with Oracle Database.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'EMBEDDING_PROFILE',
attributes =>'{"provider": "database",
"embedding_model": "MY_ONNX_MODEL"
}');
END;
/
PL/SQL procedure successfully completed.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('EMBEDDING_PROFILE');
PL/SQL procedure successfully completed.
この例では、ociをproviderとして使用します。
--Grant create any directory privilege to the user
GRANT CREATE ANY DIRECTORY to ADB_USER;
- Create a Directory object, or use an existing directory object
CREATE OR REPLACE DIRECTORY ONNX_DIR AS 'onnx_model';
-- Object storage bucket
VAR location_uri VARCHAR2(4000);
EXEC :location_uri := 'https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/eLddQappgBJ7jNi6Guz9m9LOtYe2u8LWY19GfgU8flFK4N9YgP4kTlrE9Px3pE12/n/adwc4pm/b/OML-Resources/o/';
-- Model file name
VAR file_name VARCHAR2(512);
EXEC :file_name := 'all_MiniLM_L12_v2.onnx';
-- Download ONNX model from object storage into the directory object
BEGIN
DBMS_CLOUD.GET_OBJECT(
credential_name => NULL,
directory_name => 'ONNX_DIR',
object_uri => :location_uri || :file_name);
END;
/
-- Load the ONNX model into the database
BEGIN
DBMS_VECTOR.LOAD_ONNX_MODEL(
directory => 'ONNX_DIR',
file_name => :file_name,
model_name => 'MY_ONNX_MODEL');
END;
/
-- Verify
SELECT model_name, algorithm, mining_function
FROM user_mining_models
WHERE model_name='MY_ONNX_MODEL';
–-Administrator Grants EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--Administrator Grants EXECUTE privilege DBMS_CLOUD_PIPELINE to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_PIPELINE to ADB_USER;
-- Create the object store credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OCI_CRED',
username => '<your_username>',
password => '<OCI_profile_password>'
);
END;
/
--Create GenAI credentials
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaaa...',
tenancy_ocid => 'ocid1.tenancy.oc1..aaaa...',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
--Create OCI AI profile
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"model": "meta.llama-3.3-70b-instruct",
"credential_name": "GENAI_CRED",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: MY_ONNX_MODEL"}'
);
END;
/
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_GENAI');
PL/SQL procedure successfully completed.
-- create a vector index with the vector store name, object store location and
-- object store credential
BEGIN
DBMS_CLOUD_AI.CREATE_VECTOR_INDEX(
index_name => 'MY_INDEX',
attributes => '{"vector_db_provider": "oracle",
"location": "https://swiftobjectstorage.us-phoenix-1.oraclecloud.com/v1/my_namespace/my_bucket/my_data_folder",
"object_storage_credential_name": "OCI_CRED",
"profile_name": "OCI_GENAI",
"vector_dimension": 384,
"vector_distance_metric": "cosine",
"chunk_overlap":128,
"chunk_size":1024
}');
END;
/
PL/SQL procedure successfully completed.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_GENAI');
PL/SQL procedure successfully completed.
-- Select AI answers the question with the knowledge available in the vector database.
set pages 1000
set linesize 150
SELECT AI narrate how can I deploy an oracle machine learning model;
RESPONSE
To deploy an Oracle Machine Learning model, you would first build your model within the Oracle database. Once your in-database models are
built, they become immediately available for use, for instance, through a SQL query using the prediction operators built into the SQL
language.
The model scoring, like model building, occurs directly in the database, eliminating the need for a separate engine or environment within
which the model and corresponding algorithm code operate. You can also use models from a different schema (user account) if the appropriate
permissions are in place.
Sources:
- Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt (https://objectstorage.../v1/my_namespace/my_bucket/
my_data_folder/Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt)
- Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-Database-Machine-Learning-and-APEX.txt
(https://objectstorage.../v1/my_namespace/my_bucket/my_data_folder/Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-
Database-Machine-Learning-and-APEX.txt)親トピック: Select AIの使用例
例: SQL問合せ生成の改善
これらの例は、データベース表および列のコメント、注釈、外部キーおよび参照整合性制約によって、自然言語プロンプトからのSQL問合せの生成がどのように改善されるかを示しています。
例: 表および列のコメントによるSQL生成の改善
データベース表に表および列のコメントがある場合は、DBMS_CLOUD_AI.CREATE_PROFILEファンクションの"comments":"true"パラメータを有効にして、表レベルおよび列レベルのコメントを取得します。コメントはLLMのメタデータに追加され、SQLの生成が改善されます。
-- 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例: 表および列注釈を使用したSQL問合せ生成の改善
この例では、Oracle Database 23aiで適用可能なSelect AIでの注釈の統合を示します。注釈は、LLMに送信されるメタデータに追加されます。
スキーマに注釈を含む表がある場合は、DBMS_CLOUD_AI.CREATE_PROFILE関数の"annotations":"true"を有効にして、メタデータに注釈を追加するようにSelect AIに指示します。
--
-- Annotations
--
CREATE TABLE emp2 (
empno NUMBER,
ename VARCHAR2(50) ANNOTATIONS (display 'lastname'),
salary NUMBER ANNOTATIONS ("person_salary", "column_hidden"),
deptno NUMBER ANNOTATIONS (display 'department')
)ANNOTATIONS (requires_audit 'yes', version '1.0', ówner 'HR Organization');
Table created.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'GOOGLE_ANNOTATIONS',
attributes => '{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "emp2"}],
"annotations" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('GOOGLE_ANNOTATIONS');
PL/SQL procedure successfully completed.例: 外部キーおよび参照キー制約を使用したSQL問合せ生成の改善
この例では、LLMのメタデータに外部キーおよび参照キー制約を取得することで、LLMが正確なJOIN条件を生成する機能を示します。外部キー制約と参照キー制約は、LLMに対する表間の構造化関係データを提供します。
Select AIのDBMS_CLOUD_AI.CREATE_PROFILE関数で"constraints":"true"を有効にして、外部キーおよび参照キーを取得します。
--
-- Referential Constraints
--
CREATE TABLE dept_test (
deptno NUMBER PRIMARY KEY,
dname VARCHAR2(50)
);
Table created.
CREATE TABLE emp3 (
empno NUMBER PRIMARY KEY,
ename VARCHAR2(50),
salary NUMBER,
deptno NUMBER,
CONSTRAINT emp_dept_fk FOREIGN KEY (deptno) REFERENCES dept_test(deptno)
);
Table created.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'GOOGLE_CONSTRAINTS',
attribues =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "dept_test"},
{"owner": "ADB_USER", "name": "emp3"}],
"constraints" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('GOOGLE_CONSTRAINTS');
PL/SQL procedure successfully completed.例: 関連する表メタデータの自動検出
これらの例は、Select AIが関連する表を自動的に検出し、Oracle Database 23aiの問合せに関連する特定の表に対してのみメタデータを送信する方法を示しています。この機能を有効にするには、object_list_modeをautomatedに設定します。これにより、<profile_name>_OBJECT_LIST_VECINDEXという名前のベクトル索引が自動的に作成されます。ベクトル索引は、デフォルトの属性および値(refresh_rate、similarity_threshold、match_limitなど)で初期化されます。一部の属性は、DBMS_CLOUD_AI.UPDATE_VECTOR_INDEXを使用して変更できます。詳細は、UPDATE_VECTOR_INDEXプロシージャを参照してください。
一方のプロファイルはobject_listを使用してスキーマを指定するように構成され、もう一方のプロファイルはobject_listを指定しません。ただし、同じSQL構成が必要です。
「AIの選択の前提条件の実行」を確認して、DBMS_CLOUD_AIパッケージへのアクセスを提供し、AIプロバイダへのネットワーク・アクセスを提供します。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'OCI_AUTO',
attributes=>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"object_list": [{"owner": "SH"}],
"oci_compartment_id": "ocid1.compartment.oc1..aaaa...",
"model" : "meta.llama-3.3-70b-instruct"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_AUTO');
PL/SQL procedure successfully completed.
select ai showsql how many customers in San Francisco are married;
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c."CUST_ID") AS "NUMBER_OF_CUSTOMERS"
FROM "SH"."CUSTOMERS" c
WHERE UPPER(c."CUST_CITY") = UPPER('San Francisco')
AND UPPER(c."CUST_MARITAL_STATUS") = UPPER('married')次の例では、object_listを使用せずに同じシナリオを比較します。object_listを指定しない場合、「AIの選択」では、現在のスキーマで使用可能なすべてのオブジェクトが自動的に選択されます。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'OCI_AUTO1',
attributes=>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"oci_compartment_id": "ocid1.compartment.oc1..aaaa...",
"object_list_mode": "automated",
"model" : "meta.llama-3.3-70b-instruct"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_AUTO1');
PL/SQL procedure successfully completed.
select ai showsql how many customers in San Francisco are married?;
RESPONSE
----------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(c."CUST_ID") AS "Number_of_Customers"
FROM "SH"."CUSTOMERS" c
WHERE UPPER(c."CUST_CITY") = UPPER('San Francisco')
AND UPPER(c."CUST_MARITAL_STATUS") = UPPER('Married')親トピック: Select AIの使用例
例: 合成データの生成
この例では、実データの特性と分布を模倣した合成データを生成する方法を探ります。
次の例では、スキーマにいくつかの表を作成し、AIプロバイダとしてOCI生成AIを使用してAIプロファイルを作成し、DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA関数を使用してそれらの表にデータを合成し、Select AIを使用して自然言語プロンプトに対して問合せまたはレスポンスを生成する方法を示します。
--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
例: 複数の表に対する合成データの生成
AIプロバイダ・プロファイルを作成して設定した後、DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATAを使用して複数の表のデータを生成します。「AIの選択」を問い合せたり、使用して自然言語プロンプトに応答できます。
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例: サンプル行を使用した合成データ生成のガイド
合成データの生成でAIサービスをガイドするために、表から既存のレコードをランダムに選択できます。たとえば、params引数に{"sample_rows": 5}を追加すると、表からAIプロバイダに5つのサンプル行を送信できます。この例では、Transactions表のサンプル行に基づいて10行追加を生成します。
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;
/例: ユーザー・プロンプトを使用した合成データ生成のカスタマイズ
user_prompt引数を使用すると、データ生成のための追加のルールまたは要件を指定できます。これは、単一の表に適用することも、複数の表のobject_list引数の一部として適用することもできます。たとえば、DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATAへの次のコールでは、2009年にリリースされたムービーで合成データを生成するようAIに指示します。
-- 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;
/例: 表統計を使用した合成データ品質の向上
表に列統計があるか、メタデータを含むデータベースからクローニングされている場合、Select AIはこれらの統計を使用して、元のデータとよく似ているか、または一貫性のあるデータを生成できます。
NUMBER列の場合、統計の上限値と下限値は値の範囲をガイドします。たとえば、元のEMPLOYEES表のSALARY列の範囲が1000から10000の場合、この列の合成データもこの範囲内に収まります。
値がCA、WAおよびTXのSTATE列など、個別値を持つ列の場合、合成データではこれらの特定の値が使用されます。この機能は、{"table_statistics": true/false}パラメータを使用して管理できます。デフォルトでは、表統計は有効です。
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;
/例: 列コメントを使用したデータ生成のガイド
列コメントが存在する場合、「AIの選択」には、データ生成時にLLMの追加情報を提供するために自動的に含まれます。たとえば、トランザクション表のStatus列のコメントには、successful、failed、pending、canceled、need manual checkなどの許容値がリストされます。また、コメントを追加して列をさらに説明し、正確なデータを生成するためのより正確な指示やヒントをAIサービスに提供することもできます。デフォルトでは、コメントは無効です。詳細は、オプション・パラメータを参照してください。
-- 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;
/例: 合成データ生成での一意の値の設定
大量の合成データをLLMで生成する場合、重複する値が発生する可能性があります。これを防ぐには、関連する列に一意制約を設定します。これにより、LLMレスポンスで重複値を持つ行がSelect AIによって無視されます。また、特定の列の値を制限するには、user_promptを使用するか、コメントを追加して、STATE列をCA、WAおよびTXに制限するなど、許可される値を指定できます。
-- 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'
/例: パラレル処理による合成データ生成の拡張
実行時間を短縮するために、Select AIは、合成データ生成タスクを、主キーのない表または数値の主キーを持つ表の小さなチャンクに分割します。これらのタスクは並行して実行され、AIプロバイダと対話してデータをより効率的に生成します。Autonomous Databaseサービス・レベルおよびECPUまたはOCPU設定の影響を受けるデータベース内の並列度(DOP)によって、各チャンク・プロセスのレコード数が決まります。通常、タスクを並行して実行すると、パフォーマンスが向上します。特に、多数の表にわたって大量のデータを生成する場合です。合成データ生成のパラレル処理を管理するには、オプションのパラメータとしてpriorityを設定します。オプション・パラメータを参照してください。
親トピック: Select AIの使用例
例: データ・アクセスの有効化または無効化
次の例では、管理者がデータ・アクセスを制御し、Select AIによる実際のスキーマ表のLLMへの送信を防止する方法を説明します。
データ・アクセスの無効化
スキーマ表へのアクセスを制限するには、管理者としてログインし、次の手順を実行します。
EXEC DBMS_CLOUD_AI.DISABLE_DATA_ACCESS;
PL/SQL procedure successfully completed.
データ・アクセス制限の無効化AIのnarrateアクションおよび合成データ生成を選択します。narrateアクションおよび合成データ生成でエラーが発生します。
データベース・ユーザーとしてログインし、AIプロファイルを作成および構成します。「AIの選択の前提条件の実行」を確認して、AIプロファイルを構成します。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'DATA_ACCESS',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner":"SH"}]
}');
END;
/
EXEC DBMS_CLOUD_AI.SET_PROFILE('DATA_ACCESS');
select ai how many customers;
NUM_CUSTOMERS
55500
select ai narrate what are the top 3 customers in San Francisco;
ORA-20000: Data access is disabled for SELECT AI.
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD", line 2228
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD_AI", line 13157
ORA-06512: at line 1 https://docs.oracle.com/error-help/db/ora-20000/
The stored procedure 'raise_application_error' was called which causes this error to be generated
Error at Line: 1 Column: 6次の例は、合成データを生成しようとしたときにトリガーされるエラーを示しています。
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'DATA_ACCESS_SDG',
object_name => 'CUSTOMERS_NEW',
owner_name => 'ADB_USER,
record_count => 5
);
END;
/
ERROR at line 1:
ORA-20000: Data access is disabled for SELECT AI.
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD", line 2228
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD_AI", line 13401
ORA-06512: at line 2データ・アクセスの使用可能化
次の例は、データ・アクセスの有効化を示しています。管理者としてログインし、次の手順を実行します。
EXEC DBMS_CLOUD_AI.ENABLE_DATA_ACCESS;
PL/SQL procedure successfully completed.データベース・ユーザーとしてログインし、AIプロファイルを作成および構成します。「AIの選択の前提条件の実行」を確認して、AIプロファイルを構成します。narrateアクションを実行し、合成データを個別に生成します。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'DATA_ACCESS_NEW',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner":"SH"}]
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('DATA_ACCESS_NEW');
PL/SQL procedure successfully completed.
select ai how many customers;
NUM_CUSTOMERS
55500
select ai narrate what are the top 3 customers in San Francisco;
"RESPONSE"
"The top 3 customers in San Francisco are Cody Seto, Lauren Yaskovich, and Ian Mc"
次の例は、データ・アクセスを有効にした後の正常な合成データ生成を示しています。
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'DATA_ACCESS_SDG',
object_name => 'CUSTOMERS_NEW',
owner_name => 'ADB_USER',
record_count => 5
);
END;
/
PL/SQL procedure successfully completed.親トピック: Select AIの使用例
例: AIプロファイルでのテーブルアクセスの制限
この例では、表アクセスを制限し、AIプロファイルのobject_listで指定された表のみを使用するようにLLMに指示する方法を示します。
enforce_object_listをtrueに設定して、LLMへの表アクセスを制限します。
データベース・ユーザーとして、AIプロファイルを作成および構成します。「AIの選択の前提条件の実行」を確認して、AIプロファイルを構成します。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE_ENFORCED',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "GENRE"},
{"owner": "ADB_USER", "name": "CUSTOMER"},
{"owner": "ADB_USER", "name": "PIZZA_SHOP"},
{"owner": "ADB_USER", "name": "STREAMS"},
{"owner": "ADB_USER", "name": "MOVIES"},
{"owner": "ADB_USER", "name": "ACTIONS"}],
"enforce_object_list" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.set_profile('GOOGLE_ENFORCED');
PL/SQL procedure successfully completed.
select ai showsql please list the user tables;
RESPONSE
--------------------------------------------------------------------------------------------
SELECT 'ADB_USER.GENRE' AS TABLE_NAME FROM DUAL UNION ALL SELECT 'ADB_USER.CUSTOMER' AS
TABLE_NAME FROM DUAL UNION ALL SELECT 'ADB_USER.PIZZA_SHOP' AS TABLE_NAME FROM DUAL UNION
ALL SELECT 'ADB_USER.STREAMS' AS TABLE_NAME FROM DUAL UNION ALL SELECT 'ADB_USER.MOVIES'
AS TABLE_NAME FROM DUAL
--enforce_object_listをfalseに設定すると、LLMは以前の知識に基づいて他の表およびビューを使用するように指示されます。
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE_ENFORCED1',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "GENRE"},
{"owner": "ADB_USER", "name": "CUSTOMER"},
{"owner": "ADB_USER", "name": "PIZZA_SHOP"},
{"owner": "ADB_USER", "name": "STREAMS"},
{"owner": "ADB_USER", "name": "MOVIES"},
{"owner": "ADB_USER", "name": "ACTIONS"}],
"enforce_object_list" : "false"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.set_profile('GOOGLE_ENFORCED1');
PL/SQL procedure successfully completed.
select ai showsql please list the user tables;
RESPONSE
----------------------------------
SELECT TABLE_NAME FROM USER_TABLES
親トピック: Select AIの使用例
例: 列の大文字と小文字の区別の指定
この例では、AIプロファイルの列に大/小文字の区別を設定する方法を示します。
大文字と小文字が区別されない問合せを取得するには、case_sensitive_valuesをfalseに設定します。
データベース・ユーザーとして、AIプロファイルを作成および構成します。「AIの選択の前提条件の実行」を確認して、AIプロファイルを構成します。
BEGIN
DBMS_CLOUD_AI.create_profile(
profile_name =>'GOOGLE',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "GENRE"},
{"owner": "ADB_USER", "name": "CUSTOMER"},
{"owner": "ADB_USER", "name": "PIZZA_SHOP"},
{"owner": "ADB_USER", "name": "STREAMS"},
{"owner": "ADB_USER", "name": "MOVIES"},
{"owner": "ADB_USER", "name": "ACTIONS"}],
"case_sensitive_values" : "false"
}');
END;
/
PL/SQL procedure successfully completed.
-- With "case_sensitive_values" set to "false", LLM will give back case insensitive query.
select ai showsql how many people watch Inception;
RESPONSE
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c.CUSTOMER_ID) AS "COUNT"
FROM "ADB_USER"."CUSTOMER" c
JOIN "ADB_USER"."STREAMS" s ON c.CUSTOMER_ID = s.CUSTOMER_ID
JOIN "ADB_USER"."MOVIES" m ON s.MOVIE_ID = m.MOVIE_ID
WHERE UPPER(m.TITLE) = UPPER('Inception')case_sensitive_valuesがfalseに設定されている場合でも、二重引用符を使用して大/小文字を区別する問合せを指定できます。
select ai showsql how many people watch "Inception";
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c.CUSTOMER_ID) AS "COUNT"
FROM "ADB_USER"."CUSTOMER" c JOIN "ADB_USER"."STREAMS" s ON
c.CUSTOMER_ID = s.CUSTOMER_ID JOIN "ADB_USER"."MOVIES" m ON
s.MOVIE_ID = m.MOVIE_ID WHERE m.TITLE = 'Inception'
親トピック: Select AIの使用例