Esempi di utilizzo di Select AI
Esplora l'integrazione di Select AI di Oracle con vari provider AI supportati per generare, eseguire e spiegare SQL da prompt in linguaggio naturale o chattare con l'LLM.
- Esempio: selezione di azioni AI
Questi esempi illustrano le azioni comuni di selezione AI. - Esempio: selezionare l'intelligenza artificiale con l'AI generativa OCI
Questi esempi mostrano come puoi accedere all'AI generativa OCI utilizzando la tua chiave API OCI o il principal risorsa, creare un profilo AI e generare, eseguire e spiegare SQL da prompt del linguaggio naturale o chattare utilizzando i LLM dell'AI generativa OCI. - Esempio: selezione di AI con OpenAI
In questo esempio viene illustrato come utilizzare OpenAI per generare istruzioni SQL da prompt in linguaggio naturale. - Esempio: selezione di AI con Cohere
In questo esempio viene illustrato come utilizzare Cohere per generare istruzioni SQL da prompt in linguaggio naturale. - Esempio: selezione dell'intelligenza artificiale con il servizio OpenAI di Azure
Gli esempi riportati di seguito mostrano come è possibile abilitare l'accesso al servizio OpenAI di Azure utilizzando la chiave API oppure utilizzare Azure OpenAI Service Principal, creare un profilo AI e generare SQL da prompt in linguaggio naturale. - Esempio: selezione dell'intelligenza artificiale con Google
In questo esempio viene illustrato come utilizzare Google per generare, eseguire e spiegare l'istruzione SQL da prompt in linguaggio naturale o chat utilizzando l'LLM Google Gemini. - Esempio: Select AI with Anthropic
In questo esempio viene illustrato come utilizzare Anthropic per generare, eseguire e spiegare SQL da prompt in linguaggio naturale o chat utilizzando l'LLM Claude Anthropic. - Esempio: selezione dell'intelligenza artificiale con Hugging Face
In questo esempio viene illustrato come utilizzare Hugging Face per generare, eseguire e spiegare l'istruzione SQL dai prompt del linguaggio naturale o dalla chat utilizzando l'LLM Hugging Face. - Esempio: selezione dell'intelligenza artificiale con AWS
Questo esempio mostra come utilizzare AWS per generare, eseguire e spiegare SQL da prompt in linguaggio naturale o chat utilizzando i modelli disponibili con AWS. - Esempio: selezione di AI con OpenAI-Provider compatibili
In questo esempio viene illustrato come utilizzare provider compatibili con OpenAI per generare, eseguire e spiegare SQL da prompt in linguaggio naturale o chat utilizzando i modelli disponibili con provider compatibili con OpenAI. - Esempio: abilitazione delle conversazioni in Select AI
Questi esempi illustrano l'abilitazione delle conversazioni in Select AI. - Esempio: impostazione e uso di Select AI con RAG
Questo esempio guida l'utente nell'impostazione delle credenziali, nella configurazione dell'accesso alla rete e nella creazione di un indice vettoriale per l'integrazione dei servizi cloud della memoria di vettore OCI Generative AI con OpenAI utilizzando Oracle Autonomous AI Database. - Esempio: selezione dell'intelligenza artificiale con i modelli di trasformatore nel database
In questo esempio viene illustrato come importare un modello di trasformatore pre-addestrato memorizzato nello storage degli oggetti Oracle nell'istanza di Oracle AI Database 26ai, quindi utilizzare il modello in-database importato nel profilo Select AI per generare integrazioni vettoriali per i chunk di documenti e i prompt utente. - Esempio: miglioramento della generazione delle query SQL
Questi esempi dimostrano in che modo i commenti, le annotazioni, la chiave esterna e i vincoli di integrità referenziale nelle tabelle e nelle colonne del database possono migliorare la generazione di query SQL dai prompt in linguaggio naturale. - Esempio: generazione di dati sintetici
In questo esempio viene analizzato il modo in cui è possibile generare dati sintetici che imitano le caratteristiche e la distribuzione dei dati reali. - Esempio: abilitazione o disabilitazione dell'accesso AI dati
In questo esempio viene illustrato come gli amministratori possono controllare l'accesso AI dati e impedire a Select AI di inviare tabelle di schema effettive all'LLM. - Esempio: selezione del feedback AI
In questi esempi viene illustrato come utilizzare la proceduraDBMS_CLOUD_AI.FEEDBACKe i diversi scenari di interazione con l'azionefeedbackper fornire un feedback e migliorare la successiva generazione di query SQL. - Esempio: selezione del sintetico AI
Questi esempi mostrano come utilizzare l'azionesummarizee la funzioneDBMS_CLOUD_AI.SUMMARIZE. Inoltre, personalizzare la generazione di riepilogo per il contenuto utilizzando la funzione. - Esempio: selezione della traduzione AI
In questi esempi viene illustrato come utilizzare la funzionalitàtranslate. - Esempio: limitazione dell'accesso alle tabelle nel profilo AI
In questo esempio viene illustrato come limitare l'accesso alle tabelle e come indicare all'LLM di utilizzare solo le tabelle specificate nel fileobject_listdel profilo AI. - Esempio: Specifica distinzione tra maiuscole e minuscole per le colonne
In questo esempio viene illustrato come impostare la distinzione tra maiuscole e minuscole per le colonne nel profilo AI.
Esempio: Seleziona azioni AI
Questi esempi illustrano le azioni comuni Select AI.
L'esempio seguente illustra azioni quali runsql (impostazione predefinita), showsql, narrate, chat, explainsql, feedback e summarize che è possibile eseguire con SELECT AI. Questi esempi utilizzano lo schema sh con il provider AI e gli attributi profilo specificati nella funzione DBMS_CLOUD_AI.CREATE_PROFILE. Utilizzare le azioni Seleziona AI dopo aver impostato il profilo AI utilizzando la procedura DBMS_CLOUD_AI.SET_PROFILE nella sessione corrente.
Per generare un riepilogo del testo, utilizzare SELECT AI SUMMARIZE <TEXT>.
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai showsql how many customers exist;
RESPONSE
----------------------------------------------------
SELECT COUNT(*) AS total_customers
FROM SH.CUSTOMERS
SQL> select ai narrate how many customers exist;
RESPONSE
------------------------------------------------------
There are a total of 55,500 customers in the database.
SQL> select ai chat how many customers exist;
RESPONSE
--------------------------------------------------------------------------------
It is impossible to determine the exact number of customers that exist as it con
stantly changes due to various factors such as population growth, new businesses
, and customer turnover. Additionally, the term "customer" can refer to individu
als, businesses, or organizations, making it difficult to provide a specific num
ber.
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
-- Feedback on SQL Text
-- Negative feedback example:
SQL > select ai feedback for query "select ai showsql how many watch histories in total", please use sum instead of count;
-- Positive feedback example:
SQL > select ai feedback for query "select ai showsql how many watch histories in total", the sql query generated is correct;
-- Feedback on SQL ID
-- Negative feedback example:
SQL > select ai feedback please use sum instead of count for sql_id 1v1z68ra6r9zf;
-- Positive feedback example:
SQL > select ai feedback sql query result is correct for sql_id 1v1z68ra6r9zf;
-- If not specified, use default LASTAI SQL
-- To use default LASTAI sql, make sure that set server output off;
-- Negative feedback example:
SQL > select ai feedback please use ascending sorting for ranking;
-- Positive feedback example:
SQL > select ai feedback the result is correct;
SQL> SELECT AI SUMMARIZE
Like countless other people around the globe, I stream music, and like more
than six hundred million of them I mainly use Spotify. Streaming currently
accounts for about eighty per cent of the American recording industry’s
revenue, and in recent years Spotify’s health is often consulted as a measure
for the health of the music business over all. Last spring, the International
Federation of the Phonographic Industry reported global revenues of $28.6
billion, making for the ninth straight year of growth. All of this was
unimaginable in the two-thousands, when the major record labels appeared
poorly equipped to deal with piracy and the so-called death of physical media.
On the consumer side, the story looks even rosier. Adjusted for inflation, a
... (skipped 1000 rows in the middle)
Pelly writes of some artists, in search of viral fame, who surreptitiously use
social media to effectively beta test melodies and motifs, basically putting
together songs via crowdsourcing. Artists have always fretted about the
pressure to conform, but the data-driven, music-as-content era feels
different. “You are a Spotify employee at that point,” Daniel Lopatin, who
makes abstract electronic music as Oneohtrix Point Never, told Pelly. “If your
art practice is so ingrained in the brutal reality that Spotify has outlined
for all of us, then what is the music that you’re not making? What does the
music you’re not making sound like?” Listeners might wonder something similar.
What does the music we’re not hearing sound like?;
RESPONSE
------------------------------------------------------------------------------
The music streaming industry, led by Spotify, has revolutionized the way
people consume music, with streaming accounting for 80% of the American
recording industry's revenue. However, this shift has also complicated the
lives of artists, who struggle to survive in a hyper-abundant present where
music is often valued for its convenience rather than its artistic merit.
Spotify's algorithms prioritize popularity and profitability over artistic
diversity, leading to a homogenization of music and a devaluation of the
labor that goes into creating it. Meanwhile, the company's executives reap
enormous profits, with CEO Daniel Ek's net worth rivaling that of the
wealthiest musicians. As music critic Liz Pelly argues, the streaming economy
raises important questions about autonomy, creativity, and the value of art
in a world where everything is readily available and easily accessible.Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezionare l'intelligenza artificiale con OCI Generative AI
Questi esempi mostrano come puoi accedere all'AI generativa OCI utilizzando la tua chiave API OCI o il principal risorsa, creare un profilo AI e generare, eseguire e spiegare SQL dai prompt del linguaggio naturale o dalla chat utilizzando gli LLM dell'AI generativa OCI.
Nota
Se non si specifica il parametro
Se non si specifica il parametro
model_name, OCI Generative AI utilizza il modello predefinito in base alla tabella in Selezionare il provider AI e gli LLM. Per ulteriori informazioni sui parametri, vedere Attributi profilo.
-- Create Credential with OCI API key
--
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaaa...',
tenancy_ocid => 'ocid1.tenancy.oc1..aaaa...',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GENAI',
attributes =>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('GENAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"GENAI"
--
-- Use AI
--
SQL> select ai how many customers exist;
Number of Customers
-------------------
55500
SQL> select ai how many customers in San Francisco are married;
COUNT(DISTINCTC."CUST_ID")
--------------------------
28
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c."CUST_ID")
FROM "SH"."CUSTOMERS" c
JOIN "SH"."COUNTRIES" co ON c."COUNTRY_ID" = co."COUNTRY_ID"
WHERE c."CUST_CITY" = 'San Francisco' AND c."CUST_MARITAL_STATUS" = 'married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
Here is the Oracle SQL query to find the number of customers in San Francisco who are married:
```
SELECT COUNT(*)
FROM "SH"."CUSTOMERS" c
WHERE c."CUST_CITY" = 'San Francisco'
AND c."CUST_MARITAL_STATUS" = 'Married';
```
Explanation:
* We use the `COUNT(*)` aggregate function to count the number of rows that match the conditions.
* We specify the table alias `c` for the `"SH"."CUSTOMERS"` table to make the query more readable.
* We use the `WHERE` clause to filter the rows based on two conditions:
+ `c."CUST_CITY" = 'San Francisco'`: We filter the rows where the customer's city is San Francisco.
+ `c."CUST_MARITAL_STATUS" = 'Married'`: We filter the rows where the customer's marital status is Married.
* The double quotes around the table and column names are used to make the query case-sensitive, as required in Oracle SQL.
Note: The column names and table names are assumed to be case-sensitive, so we enclose them in double quotes. If the column names and table names are not case-sensitive, you can remove the double quotes.
Collapse
This snippet was truncated for display; see it in full
SQL> select ai narrate how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------
There are 28 customers in San Francisco who are married.
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
An Autonomous AI Database is a type of database that uses artificial intelligence (AI) and machine learning (ML) to automate many of the administrative and maintenance tasks typically performed by a database administrator (DBA). This allows the database to manage itself, without human intervention, to a large extent.
Autonomous AI Databases are designed to be self-driving, self-securing, and self-repairing, which means they can:
1. **Automate administrative tasks**: Such as provisioning, patching, upgrading, and tuning, which frees up DBAs to focus on higher-level tasks.
2. **Optimize performance**: By automatically adjusting parameters, indexing, and caching to ensure optimal performance and efficiency.
3. **Detect and respond to security threats**: By using AI-powered security tools to identify and respond to potential security threats in real-time.
4. **Heal itself**: By automatically detecting and repairing errors, corruption, or other issues that may arise.
5. **Scale up or down**: To match changing workload demands, without the need for manual intervention.
The benefits of Autonomous AI Databases include:
1. **Increased efficiency**: By automating routine tasks, DBAs can focus on more strategic activities.
2. **Improved performance**: Autonomous AI Databases can optimize performance in real-time, leading to faster query response times and better overall system performance.
3. **Enhanced security**: AI-powered security tools can detect and respond to threats more quickly and effectively than human administrators.
4. **Reduced costs**: By minimizing the need for manual intervention, Autonomous AI Databases can help reduce labor costs and improve resource utilization.
5. **Improved reliability**: Autonomous AI Databases can detect and repair errors more quickly, reducing downtime and improving overall system reliability.
Oracle Autonomous AI Database is a popular example of an Autonomous AI Database, which was introduced in 2018. Other vendors, such as Amazon, Microsoft, and Google, also offer Autonomous AI Database services as part of their cloud offerings.
In summary, Autonomous AI Databases are designed to be self-managing, self-optimizing, and self-healing, which can lead to improved performance, security, and efficiency, while reducing costs and administrative burdens.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('GENAI');
PL/SQL procedure successfully completed.
Esempio: selezionare l'AI con OCI Generative AI Resource Principal
Per utilizzare il principal risorsa con OCI Generative AI, l'amministratore della tenancy di Oracle Cloud Infrastructure deve concedere l'accesso per le risorse di intelligenza artificiale generativa a un gruppo dinamico. Vedere Eseguire i prerequisiti per utilizzare il principal risorsa con Autonomous AI Database per fornire l'accesso a un gruppo dinamico.
Imposta i criteri necessari per ottenere l'accesso a tutte le risorse di intelligenza artificiale generativa. Vedere Accesso all'AI generativa per ulteriori informazioni sui criteri dell'AI generativa.
-
Per ottenere l'accesso a tutte le risorse di intelligenza artificiale generativa nell'intera tenancy, utilizzare il criterio seguente:
allow group <your-group-name> to manage generative-ai-family in tenancy -
Per ottenere l'accesso a tutte le risorse di intelligenza artificiale generativa nel compartimento, utilizzare il criterio seguente:
allow group <your-group-name> to manage generative-ai-family in compartment <your-compartment-name>
Connettersi come amministratore e abilitare il principal delle risorse OCI. Per configurare i parametri, vedere ENABLE_PRINCIPAL_AUTH Procedure.
Nota
Se non si specifica il parametro
Se non si specifica il parametro
model_name, OCI Generative AI utilizza il modello predefinito in base alla tabella in Selezionare il provider AI e gli LLM. Per ulteriori informazioni sui parametri, vedere Attributi profilo.
-- Connect as Administrator user and enable OCI resource principal.
BEGIN
DBMS_CLOUD_ADMIN.ENABLE_PRINCIPAL_AUTH(provider => 'OCI');
END;
/
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GENAI',
attributes =>'{"provider": "oci",
"credential_name": "OCI$RESOURCE_PRINCIPAL",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('GENAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"GENAI"
--
-- Use AI
--
SQL> select ai how many customers exist;
Number of Customers
-------------------
55500
SQL> select ai how many customers in San Francisco are married;
COUNT(DISTINCTC."CUST_ID")
--------------------------
28
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c."CUST_ID")
FROM "SH"."CUSTOMERS" c
JOIN "SH"."COUNTRIES" co ON c."COUNTRY_ID" = co."COUNTRY_ID"
WHERE c."CUST_CITY" = 'San Francisco' AND c."CUST_MARITAL_STATUS" = 'married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
Here is the Oracle SQL query to find the number of customers in San Francisco who are married:
```
SELECT COUNT(*)
FROM "SH"."CUSTOMERS" c
WHERE c."CUST_CITY" = 'San Francisco'
AND c."CUST_MARITAL_STATUS" = 'Married';
```
Explanation:
* We use the `COUNT(*)` aggregate function to count the number of rows that match the conditions.
* We specify the table alias `c` for the `"SH"."CUSTOMERS"` table to make the query more readable.
* We use the `WHERE` clause to filter the rows based on two conditions:
+ `c."CUST_CITY" = 'San Francisco'`: We filter the rows where the customer's city is San Francisco.
+ `c."CUST_MARITAL_STATUS" = 'Married'`: We filter the rows where the customer's marital status is Married.
* The double quotes around the table and column names are used to make the query case-sensitive, as required in Oracle SQL.
Note: The column names and table names are assumed to be case-sensitive, so we enclose them in double quotes. If the column names and table names are not case-sensitive, you can remove the double quotes.
Collapse
This snippet was truncated for display; see it in full
SQL> select ai narrate how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------
There are 28 customers in San Francisco who are married.
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
An Autonomous AI Database is a type of database that uses artificial intelligence (AI) and machine learning (ML) to automate many of the administrative and maintenance tasks typically performed by a database administrator (DBA). This allows the database to manage itself, without human intervention, to a large extent.
Autonomous AI Databases are designed to be self-driving, self-securing, and self-repairing, which means they can:
1. **Automate administrative tasks**: Such as provisioning, patching, upgrading, and tuning, which frees up DBAs to focus on higher-level tasks.
2. **Optimize performance**: By automatically adjusting parameters, indexing, and caching to ensure optimal performance and efficiency.
3. **Detect and respond to security threats**: By using AI-powered security tools to identify and respond to potential security threats in real-time.
4. **Heal itself**: By automatically detecting and repairing errors, corruption, or other issues that may arise.
5. **Scale up or down**: To match changing workload demands, without the need for manual intervention.
The benefits of Autonomous AI Databases include:
1. **Increased efficiency**: By automating routine tasks, DBAs can focus on more strategic activities.
2. **Improved performance**: Autonomous AI Databases can optimize performance in real-time, leading to faster query response times and better overall system performance.
3. **Enhanced security**: AI-powered security tools can detect and respond to threats more quickly and effectively than human administrators.
4. **Reduced costs**: By minimizing the need for manual intervention, Autonomous AI Databases can help reduce labor costs and improve resource utilization.
5. **Improved reliability**: Autonomous AI Databases can detect and repair errors more quickly, reducing downtime and improving overall system reliability.
Oracle Autonomous AI Database is a popular example of an Autonomous AI Database, which was introduced in 2018. Other vendors, such as Amazon, Microsoft, and Google, also offer Autonomous AI Database services as part of their cloud offerings.
In summary, Autonomous AI Databases are designed to be self-managing, self-optimizing, and self-healing, which can lead to improved performance, security, and efficiency, while reducing costs and administrative burdens.
--
--Clear profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('GENAI');
PL/SQL procedure successfully completed.Esempio: specificare un'area diversa per il profilo AI generativa OCI
Questo esempio mostra come specificare un'area supportata dall'AI generativa OCI nel profilo. Vedere Aree con intelligenza artificiale generativa.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'GENAI',
attributes =>'{"provider": "oci",
"object_list": [
{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}
]
"region":"eu-frankfurt-1",
"model": "meta.llama-3.3-70b-instruct",
"credential_name": "GENAI_CRED",
"oci_compartment_id": "ocid1.compartment.oc1..."}');
END;
/Esempio: selezionare l'intelligenza artificiale con OCI Generative AI utilizzando Grok Model
Questo esempio dimostra come utilizzare i modelli Grok di xAI con il supporto dell'AI generativa OCI. Rivedere Esegui prerequisiti per selezione AI.
--Create your AI Profile
BEGIN
DBMS_CLOUD_AI.create_profile(
profile_name =>'grok',
attributes =>'{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [ {"owner": "SH"}],
"oci_compartment_id": "ocid1.compartment.oc1..aaaaa...",
"model":"xai.grok-3"
}');
END;
/
PL/SQL procedure successfully completed.
--Set Profile
exec dbms_cloud_ai.set_profile('grok');
PL/SQL procedure successfully completed.
--Use Select AI
select ai how many customers exist;
TOTAL_CUSTOMERS
---------------
55500
select ai how many customers in San Francisco are married;
TOTAL_MARRIED_CUSTOMERS
-----------------------
46
select ai showsql how many customers in San Francisco are married;
RESPONSE
----------------------------------------------------------
SELECT COUNT(*) AS total_married_customers
FROM "SH"."CUSTOMERS" c
WHERE UPPER(c."CUST_CITY") = UPPER('San Francisco')
AND UPPER(c."CUST_MARITAL_STATUS") = UPPER('married')
select ai explainsql how many customers in San Francisco are married;
RESPONSE
-------------------
### Oracle SQL Query
```sql
SELECT COUNT(*) AS "Total_Married_Customers"
FROM "SH"."CUSTOMERS" "cust"
WHERE UPPER("cust"."CUST_CITY") = UPPER('San Francisco')
AND UPPER("cust"."CUST_MARITAL_STATUS") = UPPER('married')
```
### Detailed Explanation
1. **Table and Schema Naming**:
- The table `"CUSTOMERS"` is referenced with its schema name `"SH"` as `"SH"."CUSTOMERS"`. This ensures that the query explicitly points to the correct schema and table, avoiding ambiguity.
- A table alias `"cust"` is used for the `"CUSTOMERS"` table to make the query more readable and concise when referencing columns.
2. **Column Naming**:
- The result of the `COUNT(*)` function is aliased as `"Total_Married_Customers"` for clarity and readability. This descriptive name indicates exactly what the count represents.
- All column names (e.g., `"CUST_CITY"`, `"CUST_MARITAL_STATUS"`) are enclosed in double quotes to maintain case sensitivity as per Oracle's naming conventions when explicitly defined.
3. **String Comparison in WHERE Clause**:
- The strings 'San Francisco' and 'married' in the question are not enclosed in double quotes. As per the provided rules, case-insensitive comparison is required.
- Therefore, the `UPPER()` function is applied to both the column values (`"cust"."CUST_CITY"` and `"cust"."CUST_MARITAL_STATUS"`) and the literal strings ('San Francisco' and 'married') to ensure the comparison ignores case differences.
- This means that records with values like 'SAN FRANCISCO', 'san francisco', or any other case variation of 'San Francisco' will match, and similarly for 'married'.
4. **Purpose of the Query**:
- The query counts the total number of customers who are located in 'San Francisco' and have a marital status of 'married'.
- The `COUNT(*)` function is used to return the total number of rows that satisfy the conditions specified in the `WHERE` clause.
5. **Readability**:
- The query uses consistent formatting with indentation for the `WHERE` clause conditions to improve readability.
- The use of a meaningful alias (`"cust"`) and a descriptive result column name (`"Total_Married_Customers"`) makes the query easier to understand at a glance.
This query will return a single number representing the count of married customers in San Francisco, handling case variations in the data appropriately.
select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
I'm showing you the top 3 customers from San Francisco, based on their unique id
entification numbers, sorted from lowest to highest. Here's who they are:
- Lyndon Baltzer
- Mike Crocker
- Milburn Klemm
select ai chat what is Autonomous AI Database;
RESPONSE
-------------------
An **Autonomous AI Database** is a cloud-based database solution that uses artificial intelligence (AI) and machine learning (ML) to automate many of the routine tasks associated with managing and maintaining a database. It is designed to operate with minimal human intervention, allowing organizations to focus on data-driven insights and application development rather than database administration. The concept is often associated with Oracle's Autonomous AI Database, which was one of the first widely recognized implementations, but other cloud providers like AWS, Microsoft Azure, and Google Cloud also offer similar self-managing database services.
### Key Features of an Autonomous AI Database:
1. **Self-Driving:** Automates tasks such as provisioning, patching, tuning, and upgrading without manual input. It uses AI to optimize performance and adapt to workload changes in real-time.
2. **Self-Securing:** Automatically applies security updates, detects vulnerabilities, and protects against threats using built-in mechanisms like encryption and threat detection, reducing the risk of human error.
3. **Self-Repairing:** Identifies and resolves issues such as system failures or performance bottlenecks autonomously, ensuring high availability and minimizing downtime.
4. **Scalability:** Dynamically scales resources (compute and storage) up or down based on demand, optimizing cost and performance.
5. **Data Management:** Supports various data types and workloads, including transactional (OLTP), analytical (OLAP), and mixed workloads, often in a single converged database environment.
### Benefits:
- **Reduced Costs:** Minimizes the need for dedicated database administrators, lowering operational expenses.
- **Improved Performance:** AI-driven optimization ensures efficient query execution and resource allocation.
- **Enhanced Security:** Automated security features reduce the likelihood of breaches due to misconfigurations or delayed updates.
- **Increased Productivity:** Frees up IT staff to focus on innovation rather than routine maintenance.
### Use Cases:
- Businesses requiring high availability and reliability for critical applications.
- Organizations looking to modernize IT infrastructure with cloud-native solutions.
- Data analytics and machine learning projects needing scalable, optimized data storage.
- Environments where security and compliance are paramount, such as finance or healthcare.
### Example:
Oracle Autonomous AI Database, available on Oracle Cloud, offers two primary services:
- **Autonomous Transaction Processing (ATP):** Optimized for transactional workloads with high performance and reliability.
- **Autonomous AI Lakehouse:** Designed for analytics and big data workloads with automated data integration and optimization.
In essence, an Autonomous AI Database represents a shift toward intelligent, self-managing data systems that leverage automation and AI to simplify database operations, enhance security, and improve efficiency in a cloud environment. If you have a specific provider or context in mind, let me know, and I can dive deeper!Esempio: selezionare l'intelligenza artificiale con OCI Generative AI utilizzando il modello LLAMA
Questo esempio mostra la funzione chat di OCI Generative AI. Evidenzia le capacità del modello attraverso due prompt: analizzare i commenti dei clienti per rafforzare il loro sentimento e generare un paragrafo introduttivo sull'arrampicata su roccia.
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaa',
tenancy_ocid => 'ocid1.tenancy.oc1..aaa',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
PL/SQL procedure successfully completed.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'GENAI',
attributes =>'{"provider": "oci",
"object_list": [
{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}
]
"model": "meta.llama-3.3-70b-instruct",
"oci_apiformat":"GENERIC",
"credential_name": "GENAI_CRED",
"oci_compartment_id": "ocid1.compartment.oc1..."}');
END;
/
PL/SQL procedure successfully completed.
--
--Set profile
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('GENAI');
PL/SQL procedure successfully completed.
SQL> set linesize 150
SQL> SELECT AI chat what is the sentiment of this comment I am not going to waste my time filling up this three page form. Lousy idea;
SQL>
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------
The sentiment of this comment is strongly negative. The user is expressing frustration and annoyance with the idea of filling out a three-page form, an
d is explicitly stating that they consider it a "lousy idea". The use of the phrase "waste my time" also implies that they feel the task is unnecessary
and unproductive. The tone is dismissive and critical.
SQL> SELECT AI chat Write an enthusiastic introductory paragraph on how to get started with rock climbing with Athletes as the target audience;
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------
Rock climbing is an exhilarating and challenging sport that's perfect for athletes looking to push their limits and test their strength, endurance,
and mental toughness. Whether you're a seasoned athlete or just starting out, rock climbing offers a unique and rewarding experience that will have
you hooked from the very first climb. With its combination of physical and mental challenges, rock climbing is a great way to build strength, improve
flexibility, and develop problem-solving skills. Plus, with the supportive community of climbers and the breathtaking views from the top of the climb,
you'll be hooked from the very first climb. So, if you're ready to take on a new challenge and experience the thrill of adventure, then it's time to
get started with rock climbing!Uso dell'AI generativa OCI con il modello predefinito
Nell'esempio riportato di seguito viene utilizzato il modello di chat AI generativa OCI predefinito. Se non si specifica il parametro model_name, OCI Generative AI utilizza il modello predefinito in base alla tabella in Selezionare il provider AI e gli LLM.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_DEFAULT',
attributes => '{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [{"owner": "ADB_USER"}]
}');
END;
/Utilizzo dell'AI generativa OCI con il modello di chat
Nell'esempio riportato di seguito viene utilizzato cohere.command-r-plus-08-2024 come modello di chat AI generativa OCI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_COHERE_COMMAND_R_PLUS',
attributes => '{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [{"owner": "ADB_USER"}],
"model": "cohere.command-r-plus-08-2024"
}');
END;
/Uso dell'intelligenza artificiale generativa OCI con ID endpoint del modello di chat
L'esempio riportato di seguito mostra come specificare l'ID endpoint del modello di chat AI generativa OCI anziché model. Se si utilizza l'ID endpoint del modello di chat Meta Llama, specificare oci_apiformat come GENERIC.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_CHAT_ENDPOINT',
attributes => '{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [{"owner": "ADB_USER"}],
"oci_endpoint_id": "<endpoint_id>",
"oci_apiformat": "GENERIC"
}');
END;
/Uso dell'AI generativa OCI con OCID modello di chat
Questo esempio mostra come specificare l'ID endpoint del modello di chat Cohere AI generativa OCI anziché model. Se si utilizza l'ID endpoint del modello di chat Meta Llama, specificare oci_apiformat come GENERIC.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_CHAT_OCID',
attributes => '{"provider": "oci",
"credential_name": "OCI_CRED",
"object_list": [{"owner": "ADB_USER"}],
"model": "<model_ocid>",
"oci_apiformat": "COHERE"
}');
END;
/Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezionare AI con OpenAI
In questo esempio viene illustrato come utilizzare OpenAI per generare istruzioni SQL dai prompt in linguaggio naturale.
Nota
Solo un DBA può eseguire i privilegi
Solo un DBA può eseguire i privilegi
EXECUTE e la procedura ACL di rete.
--Grants EXECUTE privilege to ADB_USER
--
SQL> grant execute on DBMS_CLOUD_AI to ADB_USER;
-- Grant Network ACL for OpenAI endpoint
--
SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.openai.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create Credential for AI provider
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'OPENAI_CRED',
username => 'OPENAI',
password => '<your_api_token>');
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OPENAI',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"OPENAI"
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous AI Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous AI Database uses machine learning and automation to optimize performance, security, and availability, allowing
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for
transactional workloads and Autonomous AI Lakehouse for analytical workloads. Autonomous AI Database provides high performance, scalability, and
reliability, making it an ideal choice for modern cloud-based applications.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('OPENAI');
PL/SQL procedure successfully completed.L'esempio riportato di seguito mostra come specificare un modello diverso nel profilo AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OPENAI',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"model": "gpt-3.5-turbo",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/ Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezionare AI con Cohere
In questo esempio viene illustrato come utilizzare Cohere per generare istruzioni SQL dai prompt in linguaggio naturale.
Nota
Solo un DBA può eseguire i privilegi
Solo un DBA può eseguire i privilegi
EXECUTE e la procedura ACL di rete.
--Grants EXECUTE privilege to ADB_USER
--
SQL>GRANT execute on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'COHERE_CRED',
username => 'COHERE',
password => 'your_api_token');
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Cohere endpoint
--
SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.cohere.ai',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'COHERE',
attributes =>'{"provider": "cohere",
"credential_name": "COHERE_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "sales"},
{"owner": "SH", "name": "products"},
{"owner": "SH", "name": "countries"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('COHERE');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SQL> SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"COHERE"
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('COHERE');
PL/SQL procedure successfully completed.L'esempio riportato di seguito mostra come specificare un modello e attributi personalizzati diversi nel profilo AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'COHERE',
attributes =>
'{"provider": "cohere",
"credential_name": "COHERE_CRED",
"model": "cohere.command-a-03-2025",
"object_list": [{"owner": "ADB_USER"}],
"max_tokens":512,
"stop_tokens": [";"],
"temperature": 0.5,
"comments": true
}');
END;
/Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezionare AI con il servizio OpenAI di Azure
Gli esempi riportati di seguito mostrano come è possibile abilitare l'accesso al servizio OpenAI di Azure utilizzando la chiave API oppure utilizzare Azure OpenAI Service Principal, creare un profilo AI e generare SQL dai prompt in linguaggio naturale.
-- Create Credential for AI integration
--
EXEC
DBMS_CLOUD.CREATE_CREDENTIAL(
CREDENTIAL_NAME => 'AZURE_CRED',
username => 'AZUREAI',
password => 'your_api_token');
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for OpenAI endpoint
--
BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => '<azure_resource_name>.openai.azure.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=> 'AZUREAI',
attributes=> '{"provider": "azure",
"azure_resource_name": "<azure_resource_name>",
"azure_deployment_name": "<azure_deployment_name>"
"credential_name": "AZURE_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"conversation": "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"AZUREAI"
--
-- Use AI
--
select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
select ai chat what is Autonomous AI Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous AI Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous AI Database uses machine learning and automation to optimize performance, security, and availability, allowing
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous AI Lakehouse for analytical workloads. Autonomous AI Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.L'esempio riportato di seguito mostra come specificare un modello diverso nel profilo AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'AZUREAI',
attributes=>'{"provider": "azure",
"credential_name": "AZURE$PA",
"model": "gpt-3.5-turbo",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"azure_resource_name": "<azure_resource_name>",
"azure_deployment_name": "<azure_deployment_name>"
}');
END;
/Esempio: selezionare AI con Azure OpenAI Service Principal
Connettersi come amministratore di database per fornire l'accesso all'autenticazione del principal del servizio Azure e quindi concedere le autorizzazioni ACL di rete all'utente (ADB_USER) che desidera utilizzare Select AI. Per fornire l'accesso alle risorse di Azure, vedere Utilizzare il principal di Azure Service per accedere alle risorse di Azure.
Nota
Solo un utente DBA può eseguire i privilegi
Solo un utente DBA può eseguire i privilegi
EXECUTE e la procedura ACL di rete.
-- Connect as ADMIN user and enable Azure service principal authentication.
BEGIN
DBMS_CLOUD_ADMIN.ENABLE_PRINCIPAL_AUTH(provider => 'AZURE',
params => JSON_OBJECT('azure_tenantid' value 'azure_tenantid'));
END;
/
-- Copy the consent url from cloud_integrations view and consents the ADB-S application.
SQL> select param_value from CLOUD_INTEGRATIONS where param_name = 'azure_consent_url';
PARAM_VALUE
--------------------------------------------------------------------------------
https://login.microsoftonline.com/<tenant_id>/oauth2/v2.0/authorize?client_id=<client_id>&response_type=code&scope=User.read
-- On the Azure OpenAI IAM console, search for the Azure application name and assign the permission to the application.
-- You can get the application name in the cloud_integrations view.
SQL> select param_value from CLOUD_INTEGRATIONS where param_name = 'azure_app_name';
PARAM_VALUE
--------------------------------------------------------------------------------
ADBS_APP_DATABASE_OCID
--
-- Grant Network ACL for Azure OpenAI endpoint
--SQL> BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'azure_resource_name.openai.azure.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'AZUREAI',
attributes=>'{"provider": "azure",
"credential_name": "AZURE$PA",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"azure_resource_name": "<azure_resource_name>",
"azure_deployment_name": "<azure_deployment_name>"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.
--
-- Get Profile in current session
--
SELECT DBMS_CLOUD_AI.get_profile() from dual;
DBMS_CLOUD_AI.GET_PROFILE()
--------------------------------------------------------------------------------
"AZUREAI"
--
-- Use AI
--
select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
select ai chat what is Autonomous AI Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous AI Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous AI Database uses machine learning and automation to optimize performance, security, and availability, allowing us
ers to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous AI Lakehouse for analytical workloads. Autonomous AI Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
EXEC DBMS_CLOUD_AI.DROP_PROFILE('AZUREAI');
PL/SQL procedure successfully completed.Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezionare AI con Google
Questo esempio mostra come è possibile utilizzare Google per generare, eseguire e spiegare SQL da prompt in linguaggio naturale o chat utilizzando il LLM Google Gemini.
L'esempio seguente mostra l'utilizzo di Google come provider AI. L'esempio mostra l'uso della chiave di firma API Google per fornire l'accesso alla rete, la creazione di un profilo AI, l'utilizzo delle azioni Seleziona AI per generare query SQL dai prompt in linguaggio naturale e dalle risposte alla chat.
--Grants EXECUTE privilege to ADB_USER
--
SQL> grant EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL> BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GOOGLE_CRED',
username => 'GOOGLE',
password => '<your_api_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Google endpoint
--
SQL>
SQL> BEGIN
DBMS_NETWORK_ACL_ADB_USER.APPEND_HOST_ACE(
host => 'generativelanguage.googleapis.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
SQL> BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL> EXEC DBMS_CLOUD_AI.SET_PROFILE('GOOGLE');
PL/SQL procedure successfully completed.
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous AI Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous AI Database uses machine learning and automation to optimize performance, security, and availability, allowing
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous Lakehouse for analytical workloads. Autonomous AI Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('GOOGLE');
PL/SQL procedure successfully completed. L'esempio riportato di seguito mostra come specificare un modello diverso nel profilo AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"model": "gemini-1.5-pro",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezionare l'intelligenza artificiale con antropica
Questo esempio mostra come utilizzare Anthropic per generare, eseguire e spiegare SQL da prompt in linguaggio naturale o chattare utilizzando l'LLM Anthropic Claude.
L'esempio seguente mostra l'utilizzo di Anthropic come provider AI. L'esempio dimostra l'uso della chiave di firma API Anthropic per fornire l'accesso alla rete, la creazione di un profilo AI e l'utilizzo delle azioni Select AI per generare query SQL da prompt in linguaggio naturale e chattare utilizzando l'LLM Anthropic Claude.
Per specificare gli attributi del profilo, vedere Attributi profilo.
--Grant EXECUTE privilege to ADB_USER
SQL>GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL>BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'ANTHROPIC_CRED',
username => 'ANTHROPIC',
password => '<your api key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Anthropic endpoint
--
SQL>BEGIN
DBMS_NETWORK_ACL_ADB_USER.APPEND_HOST_ACE(
host => 'api.anthropic.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
SQL>BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'ANTHROPIC',
attributes =>'{"provider": "anthropic",
"credential_name": "ANTHROPIC_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL>EXEC DBMS_CLOUD_AI.SET_PROFILE('ANTHROPIC');
PL/SQL procedure successfully completed.
--
-- Use AI
--
SQL> select ai how many customers exist;
CUSTOMER_COUNT
--------------
55500
SQL> select ai how many customers in San Francisco are married;
MARRIED_CUSTOMERS
-----------------
18
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS married_customers_count
FROM SH.CUSTOMERS c
WHERE c.CUST_CITY = 'San Francisco'
AND c.CUST_MARITAL_STATUS = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
--------------------------------------------------------------------------------
SELECT COUNT(*) AS customer_count
FROM SH.CUSTOMERS AS c
WHERE c.CUST_STATE_PROVINCE = 'San Francisco' AND c.CUST_MARITAL_STATUS = 'Married';
Explanation:
- We use the 'SH' table alias for the 'CUSTOMERS' table for better readability.
- The query uses the 'COUNT(*)' function to count the number of rows that match the given conditions.
- The 'WHERE' clause is used to filter the results:
- 'c.CUST_STATE_PROVINCE = 'San Francisco'' filters customers who have 'San Francisco' as their state or province.
- 'c.CUST_MARITAL_STATUS = 'Married'' filters customers who have 'Married' as their marital status.
The result of this query will give you the count of customers in San Francisco who are married, using the column alias 'customer_count' for the result.
Remember to adjust the table and column names based on your actual schema if they differ from the example.
Feel free to ask if you have more questions related to SQL or database in general.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
--------------------------------------------------------------------------------
The top 3 customers in San Francisco are:
1. Hector Colven - Total amount sold: $52,025.99
2. Milburn Klemm - Total amount sold: $50,842.28
3. Gavin Xie - Total amount sold: $48,677.18
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
--------------------------------------------------------------------------------
Autonomous AI Database is a cloud-based database service provided by Oracle. It is designed to automate many of the routine tasks involved in managing a database,
such as patching, tuning, and backups. Autonomous AI Database uses machine learning and automation to optimize performance, security, and availability, allowing
users to focus on their applications and data rather than database administration tasks. It offers both Autonomous Transaction Processing (ATP) for transactional
workloads and Autonomous AI Lakehouse for analytical workloads. Autonomous AI Database provides high performance, scalability, and reliability, making it
an ideal choice for modern cloud-based applications.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('ANTHROPIC');
PL/SQL procedure successfully completed.
L'esempio riportato di seguito mostra come specificare un modello diverso nel profilo AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'ANTHROPIC',
attributes =>'{"provider": "anthropic",
"credential_name": "ANTHROPIC_CRED",
"model": "claude-3-opus-20240229",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezionare AI con Hugging Face
Questo esempio mostra come utilizzare Hugging Face per generare, eseguire e spiegare SQL da prompt in linguaggio naturale o chat utilizzando l'LLM Hugging Face.
L'esempio seguente mostra l'utilizzo di Hugging Face come provider AI. L'esempio illustra l'uso della chiave di firma API Hugging Face per fornire l'accesso alla rete, la creazione di un profilo AI e l'utilizzo delle azioni Select AI per generare query SQL dai prompt in linguaggio naturale e dalla chat utilizzando l'LLM Hugging Face.
--Grant EXECUTE privilege to ADB_USER
SQL>GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
SQL>BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'HF_CRED',
username => 'HF',
password => '<your_api_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Hugging Face endpoint
--
SQL>BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api-inference.huggingface.co',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
SQL>BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'HF',
attributes =>'{"provider": "huggingface",
"credential_name": "HF_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"model" : "Qwen/Qwen2.5-72B-Instruct"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
SQL>EXEC DBMS_CLOUD_AI.SET_PROFILE('HF');
PL/SQL procedure successfully completed.
--
-- Use AI
--SQL> select ai how many customers exist;
Customer_Count
--------------
55500
SQL> select ai how many customers in San Francisco are married;
Married_Customers
-----------------
46
SQL> select ai showsql how many customers in San Francisco are married;
RESPONSE
------------------------------------------------------------
SELECT COUNT("CUST_ID") AS "Married_Customers"
FROM "SH"."CUSTOMERS" "C"
WHERE "CUST_CITY" = 'San Francisco' AND "CUST_MARITAL_STATUS
" = 'Married'
SQL> select ai explainsql how many customers in San Francisco are married;
RESPONSE
------------------------------------------------------------
To answer the question "How many customers in San Francisco are married?", we need to query the "SH"."CUSTOMERS" table and filter the results based on the city and marital status.
We will use table aliases to make the query more readable and ensure that the string comparisons follow the specified rules.
Here is the Oracle SQL query:
```sql
SELECT COUNT("CUST_ID") AS "Married_Customers_in_San_Francisco"
FROM "SH"."CUSTOMERS" c
WHERE c."CUST_CITY" = 'San Francisco' -- Case insensitive comparison
AND UPPER(c."CUST_MARITAL_STATUS") = UPPER('Married'); --
Case insensitive comparison
```
### Explanation:
1. **Table Alias**:
RESPONSE
------------------------------------------------------------
- We use the alias `c` for the "SH"."CUSTOMERS" table to make the query more readable.
2. **Column Selection**:
- We select the count of "CUST_ID" to get the number of customers who meet the criteria. We alias this count as "Married_Customers_in_San_Francisco" for clarity.
3. **WHERE Clause**:
- **City Filter**: `c."CUST_CITY" = 'San Francisco'`
- Since 'San Francisco' is not in double quotes, we use a case-insensitive comparison.
- **Marital Status Filter**: `UPPER(c."CUST_MARITAL_STATUS") = UPPER('Married')`
- Since 'Married' is not in double quotes, we use a case-insensitive comparison by applying the `UPPER` function to both sides of the comparison.
This query will return the number of customers in San Francisco who are married.
SQL> select ai narrate what are the top 3 customers in San Francisco;
RESPONSE
------------------------------------------------------------
The top 3 customers in San Francisco are:
- Hector Colven, with a customer ID of 8866.
- Milburn Klemm, with a customer ID of 1944.
- Gavin Xie, with a customer ID of 2579.
These customers have the highest total sales among all customers in San Francisco.
SQL> select ai chat what is Autonomous AI Database;
RESPONSE
------------------------------------------------------------
An Autonomous AI Database is a type of database management system that uses artificial intelligence (AI) and machine learning (ML) to automate many of the routine tasks typically performed by database administrators (DBAs). These tasks include provisioning, patching, backups, tuning, and security. The goal of an Autonomous AI Database is to reduce the need for human intervention, minimize errors, and optimize performance,thereby allowing organizations to focus on more strategic activities.
### Key Features of Autonomous AI Databases:
RESPONSE
------------------------------------------------------------
1. **Automated Provisioning**: The database can be set up and configured automatically, reducing the time and effort required to get it up and running.
2. **Self-Patching and Updates**: The database automatically applies security patches and updates without downtime, ensuring that the system remains secure and up-to-date.
3. **Self-Tuning**: The database continuously monitors its performance and adjusts settings to optimize query execution and resource utilization.
4. **Self-Backup and Recovery**: Automated backup and recovery processes ensure that data is protected and can be restored quickly in the event of a failure.
5. **Security**: Advanced security features, including threat detection and response, are built into the database to protect against cyber threats.
6. **Scalability**: The database can automatically scale resources up or down based on demand, ensuring optimal performance and cost efficiency.
7. **Monitoring and Diagnostics**: Real-time monitoring and diagnostics help identify and resolve issues before they impact performance.
RESPONSE
------------------------------------------------------------
### Benefits of Autonomous AI Databases:
- **Reduced Operational Costs**: By automating routine tasks, the need for dedicated DBAs is reduced, lowering operational costs.
- **Improved Reliability**: Automated processes reduce the risk of human error, leading to more reliable and consistent performance.
- **Enhanced Security**: Continuous monitoring and automated security measures help protect against threats.
- **Faster Time to Market**: Automated provisioning and tuning allow applications to be deployed more quickly.
RESPONSE
------------------------------------------------------------
- **Scalability and Flexibility**: The ability to scale resources automatically ensures that the database can handle varying workloads efficiently.
### Use Cases:
- **Cloud Applications**: Autonomous AI Databases are particularly useful in cloud environments where scalability and reliability are critical.
- **Data Warehousing**: They can handle large volumes of data and complex queries, making them ideal for data warehousing and analytics.
RESPONSE
------------------------------------------------------------
- **IoT and Real-Time Data Processing**: They can process and analyze real-time data from IoT devices efficiently.
- **E-commerce**: They can handle high transaction volumes and ensure fast response times for online shopping platforms.
### Examples of Autonomous AI Databases:
- **Oracle Autonomous AI Database**: One of the first and most well-known Autonomous AI Databases, offering both transactional
and data warehousing capabilities.
- **Amazon Aurora**: A managed relational database service that includes automated scaling, patching, and backups.
- **Microsoft Azure SQL Database Managed Instance**: Provides a high level of automation and management for SQL Server databases in the cloud.
- **Google Cloud Spanner**: A globally distributed, horizontally scalable relational database that is highly available and consistent.
Autonomous AI Databases represent a significant advancement in database technology, offering organizations a more efficient, secure, and cost-effective way to manage their data.
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
SQL> EXEC DBMS_CLOUD_AI.DROP_PROFILE('HF');
PL/SQL procedure successfully completed.Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezionare AI con AWS
Questo esempio mostra come utilizzare AWS per generare, eseguire e spiegare SQL da prompt in linguaggio naturale o chat utilizzando i modelli disponibili con AWS.
L'esempio seguente mostra come utilizzare AWS come provider AI con Amazon Bedrock e i suoi modelli di base. L'esempio mostra la creazione di credenziali AWS, l'accesso alla rete, la creazione di un profilo AI e l'utilizzo di azioni Select AI per generare query SQL da prompt in linguaggio naturale e chat utilizzando i modelli di base AWS.
Per utilizzare AWS, ottenere la chiave di accesso, le chiavi segrete e l'ID del modello. Vedere Usa AWS. Utilizzare l'ID modello come attributo model nella procedura DBMS_CLOUD_AI.CREATE_PROFILE. È necessario specificare l'attributo model in modo esplicito, poiché non viene fornito alcun modello predefinito.
--Grant EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'AWS_CRED',
username => '<your_AWS_access_key>',
password => '<your_AWS_secret_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for AWS
--
BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'bedrock-runtime.us-east-1.amazonaws.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'AWS',
attributes =>'{"provider": "aws",
"credential_name": "AWS_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"model" : "anthropic.claude-v2",
"conversation" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('AWS');
PL/SQL procedure successfully completed.
--
-- Use AI
--
SELECT AI how many customers exist;
"RESPONSE"
"COUNT(*)"
55500
SELECT AI how many customers in San Francisco are married;
"RESPONSE"
"COUNT(*)"
46
SELECT AI showsql how many customers in San Francisco are married;
"RESPONSE"
"SELECT COUNT(*) AS "Number of Married Customers in San Francisco"
FROM "SH"."CUSTOMERS" C
WHERE UPPER(C."CUST_CITY") = UPPER('San Francisco')
AND UPPER(C."CUST_MARITAL_STATUS") = UPPER('Married')"
SELECT AI explainsql how many customers in San Francisco are married;
"RESPONSE""SELECT
COUNT(*) AS "Number of Married Customers in San Francisco"
FROM "SH"."CUSTOMERS" C
WHERE C."CUST_CITY" = 'San Francisco'
AND C."CUST_MARITAL_STATUS" = 'Married'
Explanation:
- Used table alias C for CUSTOMERS table
- Used easy to read column names like CUST_CITY, CUST_MARITAL_STATUS
- Enclosed table name, schema name and column names in double quotes
- Compared string values in WHERE clause without UPPER() since the values are not in double quotes
- Counted number of rows satisfying the condition and aliased the count as "Number of Married Customers in San Francisco""
SELECT AI narrate what are the top 3 customers in San Francisco;
"RESPONSE"
The top 3 customers in San Francisco ordered by credit limit in descending order are:
1. Bert Katz
2. Madallyn Ladd
3. Henrietta Snodgrass
SELECT AI chat what is Autonomous AI Database;
"RESPONSE"
"An Autonomous AI Database is a cloud database service provided by Oracle Corporation. Some key features of Oracle Autonomous AI Database include:
- Fully automated and self-driving - The database automatically upgrades, patches, tunes, and backs itself up without any human intervention required.
- Self-securing - The database uses machine learning to detect threats and automatically apply security updates.
- Self-repairing - The database monitors itself and automatically recovers from failures and errors without downtime.
- Self-scaling - The database automatically scales compute and storage resources up and down as needed to meet workload demands.
- Serverless - The database is accessed as a cloud service without having to manually provision any servers or infrastructure.
- High performance - The database uses Oracle's advanced automation and machine learning to continuously tune itself for high performance.
- Multiple workload support - Supports transaction processing, analytics, graph processing, etc in a single converged database.
- Fully managed - Oracle handles all the management and administration of the database. Users just load and access their data.
- Compatible - Supports common SQL and Oracle PL/SQL for easy migration from on-prem Oracle databases.
So in summary, an Oracle Autonomous AI Database is a fully automated, self-driving, self-securing, and self-repairing database provided as a simple cloud service. The automation provides high performance, elasticity, and availability with minimal human labor required."
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('AWS');
PL/SQL procedure successfully completed.Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezionare AI con provider compatibili con OpenAI
Questo esempio mostra come utilizzare provider compatibili con OpenAI per generare, eseguire e spiegare SQL da prompt in linguaggio naturale o chat utilizzando i modelli disponibili con provider compatibili con OpenAI.
L'esempio seguente mostra come utilizzare Fireworks AI come provider compatibile con OpenAI. Dimostra come creare credenziali utilizzando la chiave di firma API AI di Fireworks, configurare l'accesso alla rete, creare un profilo AI e utilizzare le azioni Select AI per generare query SQL da prompt in linguaggio naturale e chattare utilizzando il modello LLM AI di Fireworks.
Per utilizzare Fireworks AI, specificare provider_endpoint come attributo nella procedura DBMS_CLOUD_AI.CREATE_PROFILE anziché l'attributo provider. Vedere Usa provider compatibili con OpenAI per ottenere l'attributo. È necessario specificare l'attributo model in modo esplicito, poiché non viene fornito alcun modello predefinito.
--Grant EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--
-- Create Credential for AI provider
--
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'FIREWORKS_CRED',
username => 'FIREWORKS',
password => '<your_fireworksaiapi_key>'
);
END;
/
PL/SQL procedure successfully completed.
--
-- Grant Network ACL for Fireworks AI endpoint
--
BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.fireworks.ai',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
--
-- Create AI profile
--
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'FIREWORKS',
attributes =>'{
"credential_name": "FIREWORKS_CRED",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}],
"model" : "accounts/fireworks/models/llama-v3p1-405b-instruct",
"provider_endpoint" : "api.fireworks.ai/inference",
"conversation" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
--
-- Enable AI profile in current session
--
EXEC DBMS_CLOUD_AI.SET_PROFILE('FIREWORKS');
PL/SQL procedure successfully completed.
--
-- Use AI
--
select ai how many customers exist;
"RESPONSE"
"COUNT(*)"
55500
select ai how many customers in San Francisco are married;
"RESPONSE"
"COUNT(*)"
46
select ai showsql how many customers in San Francisco are married;
"RESPONSE"
"SELECT COUNT(*)
FROM ""SH"".""CUSTOMERS"" ""c""
WHERE UPPER(""c"".""CUST_CITY"") = UPPER('San Francisco')
AND UPPER(""c"".""CUST_MARITAL_STATUS"") = UPPER('Married')"
select ai explainsql how many customers in San Francisco are married;
"RESPONSE"
"Here is the Oracle SQL query:
```sql
SELECT COUNT(*)
FROM ""SH"".""CUSTOMERS"" ""c""
WHERE UPPER(""c"".""CUST_CITY"") = UPPER('San Francisco')
AND UPPER(""c"".""CUST_MARITAL_STATUS"") = UPPER('Married')
```
Explanation:
* We use the table alias `""c""` to refer to the `""CUSTOMERS""` table.
* We enclose the schema name `""SH""`, table name `""CUSTOMERS""`, and column names `""CUST_CITY""` and `""CUST_MARITAL_STATUS""` in double quotes to ensure case sensitivity.
* In the `WHERE` clause, we use the `UPPER()` function to perform case-insensitive comparisons for the strings `'San Francisco'` and `'Married'`, since they are not enclosed in double quotes.
* The `COUNT(*)` function returns the number of rows in the result set, which represents the number of customers in San Francisco who are married."
select ai narrate what are the top 3 customers in San Francisco;
"RESPONSE"
The top 3 customers in San Francisco are:
1. Lyndon Baltzer
2. Mike Crocker
3. Milburn Klemm
These customers are identified by their unique customer IDs, which are used to track their information and interactions. The list shows the customer's first and last names.
select ai chat what is Autonomous AI Database;
"RESPONSE"
"An Autonomous AI Database is a type of database that uses artificial intelligence (AI) and machine learning (ML) to automate many of the tasks traditionally performed by database administrators (DBAs). The goal of an Autonomous AI Database is to provide a self-managing, self-securing, and self-repairing database that can operate with minimal human intervention.
Autonomous AI Databases use advanced algorithms and machine learning techniques to:
1. **Automate database administration**: Tasks such as patching, backups, and performance tuning are automated, freeing up DBAs to focus on higher-level tasks.
2. **Predict and prevent issues**: The database uses machine learning to predict and prevent issues such as downtime, data breaches, and performance problems.
3. **Optimize performance**: The database continuously monitors and optimizes its own performance, ensuring that it is running at peak efficiency.
4. **Enhance security**: Autonomous AI Databases use advanced security features, such as encryption and access controls, to protect data from unauthorized access.
5. **Improve data management**: Autonomous AI Databases can automatically manage data, including data ingestion, processing, and storage.
The benefits of Autonomous AI Databases include:
1. **Increased efficiency**: By automating routine tasks, Autonomous AI Databases can reduce the workload of DBAs and improve overall efficiency.
2. **Improved security**: Autonomous AI Databases can detect and respond to security threats in real-time, reducing the risk of data breaches.
3. **Enhanced performance**: Autonomous AI Databases can optimize their own performance, ensuring that applications run quickly and efficiently.
4. **Reduced costs**: By automating routine tasks and improving efficiency, Autonomous AI Databases can help reduce costs associated with database management.
Examples of Autonomous AI Databases include:
1. Oracle Autonomous AI Database
2. Microsoft Azure SQL Database
3. Amazon Aurora
4. Google Cloud SQL
Overall, Autonomous AI Databases represent a significant shift in the way databases are managed and maintained, using AI and ML to automate many of the tasks traditionally performed by DBAs."
--
--Clear the profile
--
BEGIN
DBMS_CLOUD_AI.CLEAR_PROFILE;
END;
/
PL/SQL procedure successfully completed.
--
--Drop the profile
--
EXEC DBMS_CLOUD_AI.DROP_PROFILE('FIREWORKS');
PL/SQL procedure successfully completed.Argomento padre: Esempi di utilizzo di Select AI
Esempio: Abilita conversazioni in AI selezionata
Questi esempi illustrano l'abilitazione delle conversazioni in Select AI.
Prima di iniziare
Rivedere Esegui prerequisiti per selezione AI.
Nota
Un utente con privilegi di amministratore (ADMIN) deve concedere EXECUTE e abilitare la lista di controllo dell'accesso di rete (ACL, Network Access Control List).
Conversazioni basate su sessione
Crea il tuo profilo AI. Impostare l'attributo conversation su true nel profilo. Questa azione include il contenuto delle interazioni o dei prompt precedenti, inclusi potenzialmente i metadati dello schema, e impostare il profilo. Una volta abilitato il profilo, puoi iniziare a conversare con i tuoi dati. Usa il linguaggio naturale per porre domande e dare seguito alle tue esigenze.
--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.Conversazioni personalizzabili
Gli esempi riportati di seguito illustrano l'utilizzo dell'API di gestione delle conversazioni che supporta conversazioni personalizzabili. Per utilizzare Select AI per più conversazioni:
È possibile creare e impostare la conversazione nei modi seguenti:
- Crea una conversazione
- Impostare la conversazione nella sessione utente corrente
- Usa
Select AI <action> <prompt>
- Utilizzare la funzione
DBMS_CLOUD_AI.CREATE_COVERSATION, quindi impostare la conversazione utilizzandoDBMS_CLOUD_AI.SET_CONVERSATION_ID. - Chiamare direttamente la procedura
DBMS_CLOUD_AI.CREATE_CONVERSATIONper creare e impostare la conversazione in un unico passaggio.
L'esempio seguente mostra come creare una conversazione utilizzando la funzione DBMS_CLOUD_AI.CREATE_COVERSATION e impostarla utilizzando la procedura DBMS_CLOUD_AI.SET_CONVERSATION_ID.
SELECT DBMS_CLOUD_AI.CREATE_CONVERSATION; -- in 19c, run SELECT DBMS_CLOUD_AI.create_conversation FROM dual;
CREATE_CONVERSATION
------------------------------------
30C9DB6E-EA4D-AFBA-E063-9C6D46644B92
EXEC DBMS_CLOUD_AI.SET_CONVERSATION_ID('30C9DB6E-EA4D-AFBA-E063-9C6D46644B92');
PL/SQL procedure successfully completedL'esempio seguente mostra l'esecuzione della procedura DBMS_CLOUD_AI.CREATE_COVERSATION per creare e impostare direttamente conversation_id.
EXEC DBMS_CLOUD_AI.create_conversation;
PL/SQL procedure successfully completed.È inoltre possibile personalizzare gli attributi della conversazione, ad esempio title, description, retention_days e conversation_length.
SELECT DBMS_CLOUD_AI.CREATE_CONVERSATION(
attributes => '{"title":"My first conversation",
"description":"this is my first conversation",
"retention_days":5,
"conversation_length":5}');
CREATE_CONVERSATION
------------------------------------
38F8B874-7687-2A3F-E063-9C6D4664EC3AÈ possibile visualizzare se esiste una determinata conversazione eseguendo una query sulla vista DBA/USER_CLOUD_AI_CONVERSATIONS.
-- Verify the setup
SELECT conversation_id, conversation_title, description, retention_days,
conversation_length FROM DBA_CLOUD_AI_CONVERSATIONS WHERE
conversation_id = '38F8B874-7687-2A3F-E063-9C6D4664EC3A';
CONVERSATION_ID CONVERSATION_TITLE DESCRIPTION RETENTION_DAYS CONVERSATION_LENGTH
------------------------------------ ----------------------------------------------- ---------------------------------- ------------------------------ -------------------
38F8B874-7687-2A3F-E063-9C6D4664EC3A My first conversation this is my first conversation +00005 00:00:00.000000 5
È inoltre possibile verificare se una conversazione è impostata chiamando la funzione DBMS_CLOUD_AI.GET_CONVERSATION_ID.
SELECT DBMS_CLOUD_AI.GET_CONVERSATION_ID;
--------------------------------------------------------------------------------
30C9DB6E-EA4F-AFBA-E063-9C6D46644B92Esempio: utilizzo di conversazioni personalizzabili con Select AI
Dopo aver creato e impostato la conversazione e abilitato il profilo AI, è possibile iniziare a interagire con i dati. Usa il linguaggio naturale per porre domande e dare seguito alle tue esigenze.
Usare SELECT AI <ACTION> <PROMPT>.
SELECT AI CHAT What is the difference in weather between Seattle and San Francisco?;
RESPONSE
--------------------------------------------------------------------------------
Seattle and San Francisco are both located on the West Coast of the United State
s, but they have distinct weather patterns due to their unique geography and cli
mate conditions. Here are the main differences:
1. **Rainfall**: Seattle is known for its rainy reputation, with an average annu
al rainfall of around 37 inches (94 cm). San Francisco, on the other hand, recei
ves significantly less rainfall, with an average of around 20 inches (51 cm) per
year.
2. **Cloud Cover**: Seattle is often cloudy, with an average of 226 cloudy days
per year. San Francisco is also cloudy, but to a lesser extent, with an average
of 165 cloudy days per year.
......
SELECT AI CHAT Explain the difference again in one paragraph only.;
RESPONSE
--------------------------------------------------------------------------------
Seattle and San Francisco have different weather patterns despite both experienc
ing a mild oceanic climate. San Francisco tends to be slightly warmer, with aver
age temperatures ranging from 45?F to 67?F, and receives less rainfall, around 2
0 inches per year, mostly during winter. In contrast, Seattle is cooler, with te
mperatures ranging from 38?F to 64?F, and rainier, with around 37 inches of rain
fall per year, distributed throughout the year. San Francisco is also known for
its fog, particularly during summer, and receives more sunshine, around 160 sunn
y days per year, although it's often filtered through the fog. Overall, San Fran
cisco's weather is warmer and sunnier, with more pronounced seasonal variations,
while Seattle's is cooler and rainier, with more consistent temperatures throug
hout the year.Esempio: utilizzare la funzione GENERATE per confrontare due conversazioni
L'esempio seguente mostra come due conversazioni vengono utilizzate in modo intercambiabile per porre domande e verificare risposte accurate. Ogni conversazione inizia con una domanda diversa focalizzata sul confronto. In seguito, quando si pone la stessa domanda di follow-up in entrambe le conversazioni, ciascuna restituisce una risposta diversa in base al contesto precedente.
-- First conversation
SELECT DBMS_CLOUD_AI.CREATE_CONVERSATION;
CREATE_CONVERSATION
------------------------------------
30C9DB6E-EA4D-AFBA-E063-9C6D46644B92
-- Second conversation
SELECT DBMS_CLOUD_AI.CREATE_CONVERSATION;
CREATE_CONVERSATION
------------------------------------
30C9DB6E-EA4E-AFBA-E063-9C6D46644B92
-- Call generate using the first conversation.
SELECT DBMS_CLOUD_AI.GENERATE(
prompt => 'What is the difference in weather between Seattle and San Francisco?',
profile_name => 'GENAI',
action => 'CHAT',
params => '{"conversation_id":"30C9DB6E-EA4D-AFBA-E063-9C6D46644B92"}') AS RESPONSE;
RESPONSE
--------------------------------------------------------------------------------
Seattle and San Francisco, both located in the Pacific Northwest and Northern Ca
lifornia respectively, experience a mild oceanic climate. However, there are som
e notable differences in their weather patterns:
1. **Temperature**: San Francisco tends to be slightly warmer than Seattle, espe
cially during the summer months. San Francisco's average temperature ranges from
45?F (7?C) in winter to 67?F (19?C) in summer, while Seattle's average temperat
ure ranges from 38?F (3?C) in winter to 64?F (18?C) in summer.
2. **Rainfall**: Seattle is known for its rainy reputation, with an average annu
al rainfall of around 37 inches (94 cm). San Francisco receives less rainfall, w
ith an average of around 20 inches (51 cm) per year. However, San Francisco's ra
infall is more concentrated during the winter months, while Seattle's rainfall i
s more evenly distributed throughout the year.
......
-- Call generate using the second conversation.
SELECT DBMS_CLOUD_AI.GENERATE(
prompt => 'How does the cost of living compare between New York and Los Angeles?',
profile_name => 'GENAI',
action => 'CHAT',
params => '{"conversation_id":"30C9DB6E-EA4E-AFBA-E063-9C6D46644B92"}') AS RESPONSE;
RESPONSE
--------------------------------------------------------------------------------
The cost of living in New York and Los Angeles is relatively high compared to ot
her cities in the United States. However, there are some differences in the cost
of living between the two cities. Here's a comparison of the cost of living in
New York and Los Angeles:
1. Housing: The cost of housing is significantly higher in New York than in Los
Angeles. The median home price in New York is around $999,000, while in Los Ange
les it's around $849,000. Rent is also higher in New York, with the average rent
for a one-bedroom apartment being around $3,000 per month, compared to around $
2,400 per month in Los Angeles.
2. Food: The cost of food is relatively similar in both cities, with some variat
ion in the cost of certain types of cuisine. However, eating out in New York can
be more expensive, with the average cost of a meal at a mid-range restaurant be
ing around $15-20 per person, compared to around $12-18 per person in Los Angele
s.
......
-- Call generate using the first conversation.
SELECT DBMS_CLOUD_AI.GENERATE(
prompt => 'Explain the difference again in one paragraph only.',
profile_name => 'GENAI',
action => 'CHAT',
params => '{"conversation_id":"30C9DB6E-EA4D-AFBA-E063-9C6D46644B92"}') AS RESPONSE;
RESPONSE
--------------------------------------------------------------------------------
Seattle and San Francisco have different weather patterns despite both experienc
ing a mild oceanic climate. San Francisco tends to be slightly warmer, with aver
age temperatures ranging from 45?F to 67?F, and receives less rainfall, around 2
0 inches per year, mostly during winter. In contrast, Seattle is cooler, with te
mperatures ranging from 38?F to 64?F, and rainier, with around 37 inches of rain
fall per year, distributed throughout the year. San Francisco is also known for
its fog, particularly during summer, and receives more sunshine, around 160 sunn
y days per year, although it's often filtered through the fog. Overall, San Fran
cisco's weather is warmer and sunnier, with more pronounced seasonal variations,
while Seattle's is cooler and rainier, with more consistent temperatures throug
hout the year.
-- Call generate using the second conversation.
SELECT DBMS_CLOUD_AI.GENERATE(
prompt => 'Explain the difference again in one paragraph only.',
profile_name => 'GENAI',
action => 'CHAT',
params => '{"conversation_id":"30C9DB6E-EA4E-AFBA-E063-9C6D46644B92"}') AS RESPONSE;
RESPONSE
--------------------------------------------------------------------------------
The cost of living in New York is approximately 20-30% higher than in Los Angele
s, mainly due to the higher cost of housing and transportation. New York has a m
edian home price of around $999,000 and average rent of $3,000 per month for a o
ne-bedroom apartment, compared to Los Angeles' median home price of $849,000 and
average rent of $2,400 per month. While the cost of food and utilities is relat
ively similar in both cities, the cost of transportation is higher in Los Angele
s due to its car-centric culture, but the cost of public transportation is highe
r in New York. Overall, the total monthly expenses for a single person in New Yo
rk can range from $4,600, compared to around $4,050 in Los Angeles, making New Y
ork the more expensive city to live in.
È possibile chiamare la funzione DBMS_CLOUD_AI.GENERATE senza specificare una conversazione; tuttavia, in questi casi, non dovrebbe essere prevista una risposta significativa.
-- Ask SELECT AI using the second conversation.
SELECT DBMS_CLOUD_AI.GENERATE(
prompt => 'Explain the difference again in one paragraph only.',
profile_name => 'GENAI',
action => 'CHAT') AS RESPONSE;
RESPONSE
--------------------------------------------------------------------------------
There is no previous explanation to draw from, as this is the beginning of our c
onversation. If you would like to ask a question or provide a topic, I would be
happy to explain the differences related to it in one paragraph.
Esempio: verifica delle conversazioni tramite le viste DBMS_CLOUD_AI
È possibile eseguire una query sulle viste di conversazione DBMS_CLOUD_AI per esaminare la conversazione e richiedere i dettagli. Per ulteriori dettagli, vedere DBMS_CLOUD_AI Views.
Nota
La
La
Le viste con il prefisso DBA_ sono disponibili solo per gli utenti con privilegi di amministratore (ADMIN).
SELECT conversation_id, conversation_title, description FROM dba_cloud_ai_conversations;
CONVERSATION_ID
------------------------------------
CONVERSATION_TITLE
----------------------------------------------------------------------------------------------------
DESCRIPTION
--------------------------------------------------------------------------------
30C9DB6E-EA4D-AFBA-E063-9C6D46644B92
Seattle vs San Francisco Weather
The conversation discusses the comparison of weather patterns between Seattle an
d San Francisco, focusing on the differences in temperature, rainfall, fog, suns
hine, and seasonal variation between the two cities.
30C9DB6E-EA4E-AFBA-E063-9C6D46644B92
NY vs LA Cost Comparison
The conversation discusses and compares the cost of living in New York and Los A
ngeles, covering housing, food, transportation, utilities, and taxes to provide
an overall view of the expenses in both cities.
SELECT conversation_id, count(*) FROM dba_cloud_ai_conversation_prompts
GROUP BY conversation_id;
CONVERSATION_ID COUNT(*)
------------------------------------ ----------
30C9DB6E-EA4D-AFBA-E063-9C6D46644B92 2
30C9DB6E-EA4E-AFBA-E063-9C6D46644B92 2Esempio: Aggiorna dettagli conversazione
È possibile aggiornare title, description e retention_days di una conversazione utilizzando la procedura DBMS_CLOUD_AI.UPDATE_CONVERSATION. È possibile verificare l'aggiornamento eseguendo una query sulla vista conversazione DBMS_CLOUD_AI.
-- Update the second conversation's title, description and retention_days
SQL> EXEC DBMS_CLOUD_AI.update_conversation(conversation_id => '30C9DB6E-EA4E-AFBA-E063-9C6D46644B92',
attributes => '{"retention_days":20,
"description":"This a description",
"title":"a title",
"conversation_length":20}');
PL/SQL procedure successfully completed.
-- Verify the information for the second conversation
SQL> SELECT conversation_title, description, retention_days
FROM dba_cloud_ai_conversations
WHERE conversation_id = '30C9DB6E-EA4E-AFBA-E063-9C6D46644B92';
CONVERSATION_TITLE DESCRIPTION RETENTION_DAYS LENGTH
-------------------------- ------------------------------------ -------------- --------------
a title This a description 20 20Esempio: eliminazione di un prompt
È possibile eliminare un singolo prompt dalle conversazioni e verificare la modifica eseguendo una query sulla vista Conversazione DBMS_CLOUD_AI.
-- Find the latest prompt for first conversation
SELECT conversation_prompt_id FROM dba_cloud_ai_conversation_prompts
WHERE conversation_id = '30C9DB6E-EA4D-AFBA-E063-9C6D46644B92'
ORDER BY created DESC
FETCH FIRST ROW ONLY;
CONVERSATION_PROMPT_ID
------------------------------------
30C9DB6E-EA61-AFBA-E063-9C6D46644B92
-- Delete the prompt
EXEC DBMS_CLOUD_AI.DELETE_CONVERSATION_PROMPT('30C9DB6E-EA61-AFBA-E063-9C6D46644B92');
PL/SQL procedure successfully completed.
-- Verify if the prompt is deleted
SELECT conversation_prompt_id FROM dba_cloud_ai_conversation_prompts
WHERE conversation_id = '30C9DB6E-EA4D-AFBA-E063-9C6D46644B92';
-- Only one prompt now
CONVERSATION_PROMPT_ID
------------------------------------
30C9DB6E-EA5A-AFBA-E063-9C6D46644B92Esempio: eliminazione di una conversazione
È possibile eliminare l'intera conversazione, rimuovendo anche tutti i prompt ad essa associati.
-- Delete the first conversation
EXEC DBMS_CLOUD_AI.DROP_CONVERSATION('30C9DB6E-EA4D-AFBA-E063-9C6D46644B92');
PL/SQL procedure successfully completed.
-- Verify if the conversation and its prompts are removed
SELECT conversation_id FROM dba_cloud_ai_conversations;
-- We only have the second conversation now
CONVERSATION_ID
------------------------------------
30C9DB6E-EA4E-AFBA-E063-9C6D46644B92
SELECT conversation_id, count(*) FROM dba_cloud_ai_conversation_prompts GROUP BY conversation_id;
-- We only have prompts in the second conversation
CONVERSATION_ID COUNT(*)
------------------------------------ ----------
30C9DB6E-EA4E-AFBA-E063-9C6D46644B92 2Argomento padre: Esempi di utilizzo di Select AI
Esempio: impostazione e utilizzo di Select AI con RAG
Questo esempio guida l'utente nell'impostazione delle credenziali, nella configurazione dell'accesso alla rete e nella creazione di un indice vettoriale per l'integrazione dei servizi cloud della memoria di vettore AI generativa OCI con OpenAI utilizzando Oracle Autonomous AI Database.
L'impostazione termina con la creazione di un profilo AI che utilizza l'indice vettoriale per migliorare le risposte LLM. Infine, questo esempio utilizza l'azione Select AI narrate, che restituisce una risposta migliorata utilizzando le informazioni del database vettoriale specificato.
L'esempio seguente illustra la creazione e l'esecuzione di query sull'indice vettoriale in Oracle AI Database 26ai.
--Grants EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--Grants EXECUTE privilege DBMS_CLOUD_PIPELINE to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_PIPELINE to ADB_USER;
-- Create the OpenAI credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OPENAI_CRED',
username => 'OPENAI_CRED',
password => '<your_api_key>'
);
END;
/
PL/SQL procedure successfully completed.
-- Append the OpenAI endpoint
BEGIN
DBMS_NETWORK_ACL_ADMIN.APPEND_HOST_ACE(
host => 'api.openai.com',
ace => xs$ace_type(privilege_list => xs$name_list('http'),
principal_name => 'ADB_USER',
principal_type => xs_acl.ptype_db)
);
END;
/
PL/SQL procedure successfully completed.
-- Create the object store credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OCI_CRED',
username => '<your_username>',
password => '<OCI_profile_password>'
);
END;
/
PL/SQL procedure successfully completed.
-- Create the profile with the vector index.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'OPENAI_ORACLE',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"vector_index_name": "MY_INDEX",
"temperature": 0.2,
"max_tokens": 4096,
"model": "gpt-3.5-turbo-1106"
}');
END;
/
PL/SQL procedure successfully completed.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI_ORACLE');
PL/SQL procedure successfully completed.
-- create a vector index with the vector store name, object store location and
-- object store credential
BEGIN
DBMS_CLOUD_AI.CREATE_VECTOR_INDEX(
index_name => 'MY_INDEX',
attributes => '{"vector_db_provider": "oracle",
"location": "https://swiftobjectstorage.us-phoenix-1.oraclecloud.com/v1/my_namespace/my_bucket/my_data_folder",
"object_storage_credential_name": "OCI_CRED",
"profile_name": "OPENAI_ORACLE",
"vector_dimension": 1536,
"vector_distance_metric": "cosine",
"chunk_overlap":128,
"chunk_size":1024
}');
END;
/
PL/SQL procedure successfully completed.
-- After the vector index is populated, we can now query the index.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OPENAI_ORACLE');
PL/SQL procedure successfully completed.
-- Select AI answers the question with the knowledge available in the vector database.
set pages 1000
set linesize 150
SELECT AI narrate how can I deploy an oracle machine learning model;
RESPONSE
To deploy an Oracle Machine Learning model, you would first build your model within the Oracle database. Once your in-database models are built, they become immediately available for use, for instance, through a SQL query using the prediction operators built into the SQL language.
The model scoring, like model building, occurs directly in the database, eliminating the need for a separate engine or environment within which the model and corresponding algorithm code operate. You can also use models from a different schema (user account) if the appropriate permissions are in place.
Sources:
- Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt (https://objectstorage.../v1/my_namespace/my_bucket/my_data_folder/Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt)
- Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-Database-Machine-Learning-and-APEX.txt (https://objectstorage.../v1/my_namespace/my_bucket/my_data_folder/Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-Database-Machine-Learning-and-APEX.txt)
Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezionare l'intelligenza artificiale con i modelli di trasformatore nel database
In questo esempio viene illustrato come importare un modello di trasformatore pre-addestrato memorizzato nello storage degli oggetti Oracle nell'istanza di Oracle AI Database 26ai, quindi utilizzare il modello in-database importato nel profilo Select AI per generare integrazioni vettoriali per i chunk di documenti e i prompt utente.
Per utilizzare i modelli di trasformatore nel database nel profilo Select AI, assicurarsi di disporre dei seguenti elementi:
-
il modello pre-addestrato importato nell'istanza di Oracle AI Database 26ai.
-
facoltativamente, l'accesso allo storage degli oggetti Oracle.
Importa un modello di trasformatore pre-addestrato in Oracle AI Database 26ai da Oracle Object Storage
Rivedere i passi in Importa modelli pre-addestrati in formato ONNX per la generazione vettoriale nel database e nel blog Modello di generazione incorporamento predefinito per Oracle AI Database 26ai per importare un modello di trasformatore pre-addestrato nel database.
L'esempio riportato di seguito mostra come importare un modello di trasformatore predefinito dallo storage degli oggetti Oracle nel database e quindi visualizzare il modello importato.
- 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';Usa modelli trasformatore nel database in profili AI selezionati
Questi esempi illustrano come utilizzare i modelli di trasformatori nel database all'interno di un profilo Select AI. Un profilo è configurato solo per generare incorporamenti vettoriali, mentre l'altro supporta sia le azioni Select AI che la creazione di indici vettoriali.
Rivedere Esegui prerequisiti per selezione AI per completare i prerequisiti.
Di seguito è riportato un esempio per generare solo incorporamenti vettoriali:
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'EMBEDDING_PROFILE',
attributes => '{"provider" : "database",
"embedding_model": "MY_ONNX_MODEL"}'
);
END;
/Di seguito è riportato un esempio per le azioni Select AI generali e la generazione dell'indice vettoriale in cui è possibile specificare un provider AI supportato. In questo esempio vengono utilizzati il profilo e le credenziali AI Gen OCI. Per la lista dei provider supportati, vedere Seleziona il provider AI e gli LLM. Tuttavia, se si desidera utilizzare il modello di trasformatore nel database per generare incorporamenti vettoriali, utilizzare "database: <MY_ONNX_MODEL>" nell'attributo embedding_model:
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaaa...',
tenancy_ocid => 'ocid1.tenancy.oc1..aaaa...',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"model": "meta.llama-3.3-70b-instruct",
"credential_name": "GENAI_CRED",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: MY_ONNX_MODEL"}'
);
END;
/Usa Select AI con un modello di trasformatore nel database da un altro schema
Questo esempio mostra come utilizzare Select AI con un modello di trasformatore nel database se un altro proprietario dello schema è proprietario del modello. Specificare schema_name.object_name come nome completamente qualificato del modello nell'attributo embedding_model. Se l'utente corrente è il proprietario dello schema o il proprietario del modello, è possibile omettere il nome dello schema.
Assicurarsi di disporre dei privilegi riportati di seguito se un altro proprietario dello schema è proprietario del modello.
- privilegio di sistema
CREATE ANY MINING MODEL - privilegio di sistema
SELECT ANY MINING MODEL - privilegio dell'oggetto
SELECT MINING MODELsul modello specifico
Per concedere un privilegio di sistema, è necessario che all'utente sia stato concesso il privilegio di sistema ADMIN OPTION oppure il privilegio di sistema GRANT ANY PRIVILEGE.
Per esaminare i privilegi, vedere Privilegi di sistema per Oracle Machine Learning for SQL.
Le istruzioni seguenti consentono a ADB_USER1 di assegnare un punteggio ai dati e visualizzare i dettagli del modello in qualsiasi schema, purché ai dati sia stato concesso l'accesso SELECT. Tuttavia, ADB_USER1 può creare modelli solo nello schema ADB_USER1.
GRANT CREATE MINING MODEL TO ADB_USER1;
GRANT SELECT ANY MINING MODEL TO ADB_USER1;
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"credential_name": "GENAI_CRED",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: ADB_USER1.MY_ONNX_MODEL"}'
);
END;
/L'esempio riportato di seguito mostra come specificare il nome oggetto modello con distinzione tra maiuscole e minuscole.
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;
/Esempi end-to-end con diversi provider AI
Questi esempi illustrano i passi end-to-end per l'utilizzo del modello di trasformatore nel database con Select AI RAG. Un profilo utilizza database come provider creato in modo esclusivo per generare vettori di incorporamento, mentre l'altro profilo utilizza oci come provider creato per le azioni Select AI e l'indice vettoriale.
Rivedere Esegui prerequisiti per Seleziona AI per fornire i privilegi necessari.
--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.
In questo esempio viene utilizzato oci come provider.
--Grant create any directory privilege to the user
GRANT CREATE ANY DIRECTORY to ADB_USER;
- Create a Directory object, or use an existing directory object
CREATE OR REPLACE DIRECTORY ONNX_DIR AS 'onnx_model';
-- Object storage bucket
VAR location_uri VARCHAR2(4000);
EXEC :location_uri := 'https://adwc4pm.objectstorage.us-ashburn-1.oci.customer-oci.com/p/eLddQappgBJ7jNi6Guz9m9LOtYe2u8LWY19GfgU8flFK4N9YgP4kTlrE9Px3pE12/n/adwc4pm/b/OML-Resources/o/';
-- Model file name
VAR file_name VARCHAR2(512);
EXEC :file_name := 'all_MiniLM_L12_v2.onnx';
-- Download ONNX model from object storage into the directory object
BEGIN
DBMS_CLOUD.GET_OBJECT(
credential_name => NULL,
directory_name => 'ONNX_DIR',
object_uri => :location_uri || :file_name);
END;
/
-- Load the ONNX model into the database
BEGIN
DBMS_VECTOR.LOAD_ONNX_MODEL(
directory => 'ONNX_DIR',
file_name => :file_name,
model_name => 'MY_ONNX_MODEL');
END;
/
-- Verify
SELECT model_name, algorithm, mining_function
FROM user_mining_models
WHERE model_name='MY_ONNX_MODEL';
–-Administrator Grants EXECUTE privilege to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_AI to ADB_USER;
--Administrator Grants EXECUTE privilege DBMS_CLOUD_PIPELINE to ADB_USER
GRANT EXECUTE on DBMS_CLOUD_PIPELINE to ADB_USER;
-- Create the object store credential
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'OCI_CRED',
username => '<your_username>',
password => '<OCI_profile_password>'
);
END;
/
--Create GenAI credentials
BEGIN
DBMS_CLOUD.CREATE_CREDENTIAL(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1..aaaa...',
tenancy_ocid => 'ocid1.tenancy.oc1..aaaa...',
private_key => '<your_api_key>',
fingerprint => '<your_fingerprint>'
);
END;
/
--Create OCI AI profile
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'OCI_GENAI',
attributes => '{"provider": "oci",
"model": "meta.llama-3.3-70b-instruct",
"credential_name": "GENAI_CRED",
"vector_index_name": "MY_INDEX",
"embedding_model": "database: MY_ONNX_MODEL"}'
);
END;
/
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_GENAI');
PL/SQL procedure successfully completed.
-- create a vector index with the vector store name, object store location and
-- object store credential
BEGIN
DBMS_CLOUD_AI.CREATE_VECTOR_INDEX(
index_name => 'MY_INDEX',
attributes => '{"vector_db_provider": "oracle",
"location": "https://swiftobjectstorage.us-phoenix-1.oraclecloud.com/v1/my_namespace/my_bucket/my_data_folder",
"object_storage_credential_name": "OCI_CRED",
"profile_name": "OCI_GENAI",
"vector_dimension": 384,
"vector_distance_metric": "cosine",
"chunk_overlap":128,
"chunk_size":1024
}');
END;
/
PL/SQL procedure successfully completed.
-- Set profile
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_GENAI');
PL/SQL procedure successfully completed.
-- Select AI answers the question with the knowledge available in the vector database.
set pages 1000
set linesize 150
SELECT AI narrate how can I deploy an oracle machine learning model;
RESPONSE
To deploy an Oracle Machine Learning model, you would first build your model within the Oracle database. Once your in-database models are
built, they become immediately available for use, for instance, through a SQL query using the prediction operators built into the SQL
language.
The model scoring, like model building, occurs directly in the database, eliminating the need for a separate engine or environment within
which the model and corresponding algorithm code operate. You can also use models from a different schema (user account) if the appropriate
permissions are in place.
Sources:
- Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt (https://objectstorage.../v1/my_namespace/my_bucket/
my_data_folder/Manage-your-models-with-Oracle-Machine-Learning-on-Autonomous-Database.txt)
- Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-Database-Machine-Learning-and-APEX.txt
(https://objectstorage.../v1/my_namespace/my_bucket/my_data_folder/Develop-and-deploy-machine-learning-models-using-Oracle-Autonomous-
Database-Machine-Learning-and-APEX.txt)Argomento padre: Esempi di utilizzo di Select AI
Esempio: miglioramento della generazione delle query SQL
Questi esempi illustrano in che modo i commenti, le annotazioni, la chiave esterna e i vincoli di integrità referenziale nelle tabelle e nelle colonne del database possono migliorare la generazione di query SQL dai prompt in linguaggio naturale.
Esempio: miglioramento della generazione SQL con commenti tabella e colonna
Se nelle tabelle del database sono presenti commenti a tabelle e colonne, abilitare il parametro "comments":"true" nella funzione DBMS_CLOUD_AI.CREATE_PROFILE per recuperare i commenti a livello di tabella e colonna. I commenti vengono aggiunti ai metadati dell'LLM per una migliore generazione SQL.
-- Adding comments to table 1, table 2, and table 3. Table 1 has 3 columns, table 2 has 7 columns, table 3 has 2 columns.
-- TABLE1
COMMENT ON TABLE table1 IS 'Contains movies, movie titles and the year it was released';
COMMENT ON COLUMN table1.c1 IS 'movie ids. Use this column to join to other tables';
COMMENT ON COLUMN table1.c2 IS 'movie titles';
COMMENT ON COLUMN table1.c3 IS 'year the movie was released';
-- TABLE2
COMMENT ON TABLE table2 IS 'transactions for movie views - also known as streams';
COMMENT ON COLUMN table2.c1 IS 'day the movie was streamed';
COMMENT ON COLUMN table2.c2 IS 'genre ids. Use this column to join to other tables';
COMMENT ON COLUMN table2.c3 IS 'movie ids. Use this column to join to other tables';
COMMENT ON COLUMN table2.c4 IS 'customer ids. Use this column to join to other tables';
COMMENT ON COLUMN table2.c5 IS 'device used to stream, watch or view the movie';
COMMENT ON COLUMN table2.c6 IS 'sales from the movie';
COMMENT ON COLUMN table2.c7 IS 'number of views, watched, streamed';
-- TABLE3
COMMENT ON TABLE table3 IS 'Contains the genres';
COMMENT ON COLUMN table3.c1 IS 'genre id. use this column to join to other tables';
COMMENT ON COLUMN table3.c2 IS 'name of the genre';
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name => 'myprofile',
attributes =>
'{"provider": "azure",
"azure_resource_name": "my_resource",
"azure_deployment_name": "my_deployment",
"credential_name": "my_credential",
"comments":"true",
"object_list": [
{"owner": "moviestream", "name": "table1"},
{"owner": "moviestream", "name": "table2"},
{"owner": " moviestream", "name": "table3"}
]
}'
);
DBMS_CLOUD_AI.SET_PROFILE(
profile_name => 'myprofile'
);
END;
/
--Prompts
select ai what are our total views;
RESPONSE
-------------------------------------------------
TOTAL_VIEWS
-----------
97890562
select ai showsql what are our total views;
RESPONSE
-------------------------------------------------------------------------
SELECT SUM(QUANTITY_SOLD) AS total_views
FROM "moviestream"."table"
select ai what are our total views broken out by device;
DEVICE TOTAL_VIEWS
-------------------------- -----------
mac 14719238
iphone 20793516
ipad 15890590
pc 14715169
galaxy 10587343
pixel 10593551
lenovo 5294239
fire 5296916
8 rows selected.
select ai showsql what are our total views broken out by device;
RESPONSE
---------------------------------------------------------------------------------------
SELECT DEVICE, COUNT(*) AS TOTAL_VIEWS
FROM "moviestream"."table"
GROUP BY DEVICEEsempio: miglioramento della generazione di query SQL con annotazioni di tabella e colonna
Questo esempio mostra l'integrazione delle annotazioni in Select AI, applicabile in Oracle AI Database 26ai. Le annotazioni vengono aggiunte ai metadati inviati al LLM.
Se nello schema è presente una tabella con annotazioni, abilitare "annotations":"true" nella funzione DBMS_CLOUD_AI.CREATE_PROFILE per indicare a Select AI di aggiungere annotazioni AI metadati.
--
-- 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.Esempio: miglioramento della generazione di query SQL con chiave esterna e vincoli chiave referenziale
Questo esempio dimostra la capacità dell'LLM di generare condizioni JOIN accurate recuperando i vincoli della chiave esterna e della chiave referenziale nei metadati dell'LLM. I vincoli di chiave esterna e di chiave referenziale forniscono dati strutturati sulle relazioni tra le tabelle e l'LLM.
Abilitare "constraints":"true" nella funzione DBMS_CLOUD_AI.CREATE_PROFILE per Selezionare AI per recuperare la chiave esterna e la chiave referenziale.
--
-- 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.Esempio: rilevamento automatico dei metadati delle tabelle rilevanti
Questi esempi mostrano come Select AI rileva automaticamente le tabelle pertinenti e invia metadati solo per quelle tabelle specifiche rilevanti per la query in Oracle AI Database 26ai. Per abilitare questa funzione, impostare object_list_mode su automated. In questo modo viene creato automaticamente un indice vettoriale denominato <profile_name>_OBJECT_LIST_VECINDEX. L'indice vettoriale viene inizializzato con attributi e valori predefiniti quali refresh_rate, similarity_threshold e match_limit. È possibile modificare alcuni attributi tramite DBMS_CLOUD_AI.UPDATE_VECTOR_INDEX. Per ulteriori informazioni, vedere UPDATE_VECTOR_INDEX Procedure.
Un profilo è configurato per utilizzare object_list per specificare lo schema o gli oggetti nello schema, mentre l'altro non specifica object_list. Tuttavia, è previsto lo stesso costrutto SQL.
Rivedere Esegui prerequisiti per Seleziona AI per fornire l'accesso al package DBMS_CLOUD_AI e fornire l'accesso di rete al provider AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'OCI_AUTO',
attributes=>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"object_list": [{"owner": "SH"}],
"oci_compartment_id": "ocid1.compartment.oc1..aaaa...",
"model" : "meta.llama-3.3-70b-instruct"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_AUTO');
PL/SQL procedure successfully completed.
select ai showsql how many customers in San Francisco are married;
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c."CUST_ID") AS "NUMBER_OF_CUSTOMERS"
FROM "SH"."CUSTOMERS" c
WHERE UPPER(c."CUST_CITY") = UPPER('San Francisco')
AND UPPER(c."CUST_MARITAL_STATUS") = UPPER('married')Nell'esempio seguente viene confrontato lo stesso scenario senza utilizzare object_list. Quando non si specifica object_list, Select AI sceglie automaticamente tutti gli oggetti disponibili per lo schema corrente.
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')Argomento padre: Esempi di utilizzo di Select AI
Esempio: Genera dati sintetici
In questo esempio viene illustrato come generare dati sintetici che imitano le caratteristiche e la distribuzione dei dati reali.
L'esempio riportato di seguito mostra come creare alcune tabelle nello schema, utilizzare OCI Generative AI come provider AI per creare un profilo AI, sintetizzare i dati in tali tabelle utilizzando la funzione DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA ed eseguire query o generare risposte AI prompt in linguaggio naturale con Select AI.
--Create tables or use cloned tables
CREATE TABLE ADB_USER.Director (
director_id INT PRIMARY KEY,
name VARCHAR(100)
);
CREATE TABLE ADB_USER.Movie (
movie_id INT PRIMARY KEY,
title VARCHAR(100),
release_date DATE,
genre VARCHAR(50),
director_id INT,
FOREIGN KEY (director_id) REFERENCES ADB_USER.Director(director_id)
);
CREATE TABLE ADB_USER.Actor (
actor_id INT PRIMARY KEY,
name VARCHAR(100)
);
CREATE TABLE ADB_USER.Movie_Actor (
movie_id INT,
actor_id INT,
PRIMARY KEY (movie_id, actor_id),
FOREIGN KEY (movie_id) REFERENCES ADB_USER.Movie(movie_id),
FOREIGN KEY (actor_id) REFERENCES ADB_USER.Actor(actor_id)
);
-- Create the GenAI credential
BEGIN
DBMS_CLOUD.create_credential(
credential_name => 'GENAI_CRED',
user_ocid => 'ocid1.user.oc1....',
tenancy_ocid => 'ocid1.tenancy.oc1....',
private_key => 'vZ6cO...',
fingerprint => '86:7d:...'
);
END;
/
-- Create a profile
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GENAI',
attributes =>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"object_list": [{"owner": "ADB_USER",
"oci_compartment_id": "ocid1.compartment.oc1...."}]
}');
END;
/
EXEC DBMS_CLOUD_AI.set_profile('GENAI');
-- Run the API for single table
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_name => 'Director',
owner_name => 'ADB_USER',
record_count => 5
);
END;
/
PL/SQL procedure successfully completed.
-- Query the table to see results
SQL> SELECT * FROM ADB_USER.Director;
DIRECTOR_ID NAME
----------- ----------------------------------------------------------------------------------------------------
1 John Smith
2 Emily Chen
3 Michael Brown
4 Sarah Taylor
5 David Lee
-- Or ask select ai to show the results
SQL> select ai how many directors are there;
NUMBER_OF_DIRECTORS
-------------------
5
Esempio: generazione di dati sintetici per più tabelle
Dopo aver creato e impostato il profilo del provider AI, utilizzare DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA per generare dati per più tabelle. È possibile eseguire una query o utilizzare Seleziona intelligenza artificiale per rispondere AI prompt in linguaggio naturale.
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
----------------
10Esempio: generazione di dati sintetici guida con righe campione
Per guidare il servizio AI nella generazione di dati sintetici, è possibile selezionare casualmente i record esistenti da una tabella. Ad esempio, aggiungendo {"sample_rows": 5} all'argomento params, è possibile inviare 5 righe di esempio da una tabella al provider AI. In questo esempio vengono generate 10 righe aggiuntive in base alle righe di esempio della tabella Transactions.
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_name => 'Transactions',
owner_name => 'ADB_USER',
record_count => 10,
params => '{"sample_rows":5}'
);
END;
/Esempio: personalizzazione della generazione di dati sintetici con i prompt utente
L'argomento user_prompt consente di specificare regole o requisiti aggiuntivi per la generazione dei dati. Questo può essere applicato a una singola tabella o come parte dell'argomento object_list per più tabelle. Ad esempio, nelle chiamate a DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA riportate di seguito, il prompt indica all'AI di generare dati sintetici sui filmati rilasciati nel 2009.
-- Definition for the Movie table CREATE TABLE Movie
CREATE TABLE Movie (
movie_id INT PRIMARY KEY,
title VARCHAR(100),
release_date DATE,
genre VARCHAR(50),
director_id INT,
FOREIGN KEY (director_id) REFERENCES Director(director_id)
);
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_name => 'Movie',
owner_name => 'ADB_USER',
record_count => 10,
user_prompt => 'all movies are released in 2009',
params => '{"sample_rows":5}'
);
END;
/
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_list => '[{"owner": "ADB_USER", "name": "Director","record_count":5},
{"owner": "ADB_USER", "name": "Movie_Actor","record_count":5},
{"owner": "ADB_USER", "name": "Actor","record_count":10},
{"owner": "ADB_USER", "name": "Movie","record_count":5,"user_prompt":"all movies are released in 2009"}]'
);
END;
/Esempio: miglioramento della qualità dei dati sintetici mediante le statistiche delle tabelle
Se una tabella contiene statistiche di colonna o è duplicata da un database che include metadati, Select AI può utilizzare queste statistiche per generare dati che assomigliano o sono coerenti con i dati originali.
Per le colonne NUMBER, i valori massimo e minimo delle statistiche determinano l'intervallo di valori. Ad esempio, se la colonna SALARY nella tabella EMPLOYEES originale è compresa tra 1000 e 10000, anche i dati sintetici per questa colonna rientrano in questo intervallo.
Per le colonne con valori distinti, ad esempio una colonna STATE con valori CA, WA e TX, i dati sintetici utilizzeranno questi valori specifici. È possibile gestire questa funzione utilizzando il parametro {"table_statistics": true/false}. Per impostazione predefinita, le statistiche della tabella sono abilitate.
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;
/Esempio: usa commenti colonna per generazione dati guida
Se esistono commenti di colonna, la funzione Seleziona AI li include automaticamente per fornire informazioni aggiuntive per l'LLM durante la generazione dei dati. Ad esempio, un commento sulla colonna Status in una tabella Transazione potrebbe elencare valori consentiti quali successful, failed, pending, canceled e need manual check. È inoltre possibile aggiungere commenti per spiegare ulteriormente la colonna, fornendo AI servizi AI istruzioni o suggerimenti più precisi per la generazione di dati accurati. Per impostazione predefinita, i commenti sono disabilitati. Per ulteriori informazioni, vedere Parametri facoltativi.
-- 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;
/Esempio: impostazione di valori univoci nella generazione di dati sintetici
Quando si generano grandi quantità di dati sintetici con LLM, è probabile che si verifichino valori duplicati. Per evitare questo problema, impostare un vincolo univoco sulla colonna pertinente. Ciò garantisce che Select AI ignori le righe con valori duplicati nella risposta LLM. Inoltre, per limitare i valori per determinate colonne, è possibile utilizzare user_prompt o aggiungere commenti per specificare i valori consentiti, ad esempio la limitazione di una colonna STATE a CA, WA e TX.
-- Use 'user_prompt'
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'GENAI',
object_name => 'employees',
owner_name => 'ADB_USER',
user_prompt => 'the value for state should either be CA, WA, or TX',
record_count => 10
);
END;
/
-- Use comment on column
COMMENT ON COLUMN EMPLOYEES.state IS 'the value for state should either be CA, WA, or TX'
/Esempio: miglioramento della generazione di dati sintetici mediante elaborazione parallela
Per ridurre il runtime, Select AI suddivide i task di generazione dei dati sintetici in chunk più piccoli per le tabelle senza chiavi primarie o con chiavi primarie numeriche. Queste attività vengono eseguite in parallelo, interagendo con il provider AI per generare dati in modo più efficiente. Il grado di parallelismo (DOP) nel database, influenzato dal livello di servizio di Autonomous AI Database e dalle impostazioni ECPU o OCPU, determina il numero di record di ogni processo chunk. L'esecuzione di attività in parallelo in genere migliora le prestazioni, soprattutto quando si generano grandi quantità di dati in molte tabelle. Per gestire l'elaborazione parallela della generazione di dati sintetici, impostare priority come parametro facoltativo. Vedere Parametri facoltativi.
Argomento padre: Esempi di utilizzo di Select AI
Esempio: abilitare o disabilitare l'accesso ai dati
In questo esempio viene illustrato come gli amministratori possono controllare l'accesso AI dati e impedire a Select AI di inviare tabelle di schema effettive all'LLM.
Disabilitazione dell'accesso ai dati
Per limitare l'accesso alle tabelle dello schema, eseguire il login come amministratore ed eseguire la procedura riportata di seguito.
EXEC DBMS_CLOUD_AI.DISABLE_DATA_ACCESS;
PL/SQL procedure successfully completed.
Disabilitazione dei limiti di accesso ai dati Selezionare l'azione narrate di AI e la generazione di dati sintetici. L'azione narrate e la generazione di dati sintetici generano un errore.
Eseguire il login come utente del database, creare e configurare il profilo AI. Rivedere Esegui prerequisiti per Seleziona AI per configurare il profilo AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'DATA_ACCESS',
attributes =>'{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner":"SH"}]
}');
END;
/
EXEC DBMS_CLOUD_AI.SET_PROFILE('DATA_ACCESS');
select ai how many customers;
NUM_CUSTOMERS
55500
select ai narrate what are the top 3 customers in San Francisco;
ORA-20000: Data access is disabled for SELECT AI.
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD", line 2228
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD_AI", line 13157
ORA-06512: at line 1 https://docs.oracle.com/error-help/db/ora-20000/
The stored procedure 'raise_application_error' was called which causes this error to be generated
Error at Line: 1 Column: 6L'esempio seguente mostra gli errori che vengono attivati quando si tenta di generare dati sintetici.
BEGIN
DBMS_CLOUD_AI.GENERATE_SYNTHETIC_DATA(
profile_name => 'DATA_ACCESS_SDG',
object_name => 'CUSTOMERS_NEW',
owner_name => 'ADB_USER,
record_count => 5
);
END;
/
ERROR at line 1:
ORA-20000: Data access is disabled for SELECT AI.
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD", line 2228
ORA-06512: at "C##CLOUD$SERVICE.DBMS_CLOUD_AI", line 13401
ORA-06512: at line 2Abilitazione dell'accesso ai dati
L'esempio seguente mostra l'abilitazione dell'accesso ai dati. Eseguire il login come amministratore ed eseguire la procedura riportata di seguito.
EXEC DBMS_CLOUD_AI.ENABLE_DATA_ACCESS;
PL/SQL procedure successfully completed.Eseguire il login come utente del database, creare e configurare il profilo AI. Rivedere Esegui prerequisiti per Seleziona AI per configurare il profilo AI. Eseguire l'azione narrate e generare separatamente i dati sintetici.
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"
L'esempio seguente mostra la generazione di dati sintetici riuscita dopo aver abilitato l'accesso ai dati.
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.Argomento padre: Esempi di utilizzo di Select AI
Esempio: selezione del feedback AI
In questi esempi viene illustrato come utilizzare la procedura DBMS_CLOUD_AI.FEEDBACK e i diversi scenari dell'azione feedback per fornire un feedback e migliorare la successiva generazione delle query SQL.
Prima di iniziare
Rivedere Esegui prerequisiti per selezione AI.
Esempio: fornire un feedback negativo
L'esempio seguente mostra come fornire correzioni all'istruzione SQL generata come feedback (feedback negativo) utilizzando feedback_type come negative e fornendo la query SQL.
Per aggiungere il feedback al profilo AI denominato OCI_FEEDBACK1, chiamare la procedura DBMS_CLOUD_AI.FEEDBACK con il parametro sql_text contenente il prompt. Per ulteriori informazioni sugli attributi, vedere Procedura FEEDBACK. Quindi, è possibile recuperare le colonne content e attributes dalla tabella <profile_name>_FEEDBACK_VECINDEX$VECTAB, che è collegata a tale query SQL specifica. Selezionare AI per creare automaticamente questa tabella vettoriale la prima volta che si utilizza la funzione di feedback. Per ulteriori informazioni, vedere Indice vettoriale per FEEDBACK.
SQL> select ai showsql how many movies;
RESPONSE
------------------------------------------------------------------------
SELECT COUNT(m."MOVIE_ID") AS "Number of Movies" FROM "ADB_USER"."MOVIES" m
SQL> exec DBMS_CLOUD_AI.FEEDBACK(profile_name=>'OCI_FEEDBACK1', sql_text=> 'select ai showsql how many movies', feedback_type=> 'negative', response=>'SELECT SUM(1) FROM "ADB_USER"."MOVIES"');
PL/SQL procedure successfully completed.
SQL> select CONTENT, ATTRIBUTES from OCI_FEEDBACK1_FEEDBACK_VECINDEX$VECTAB where JSON_VALUE(attributes, '$.sql_text') = 'select ai showsql how many movies';
CONTENT
----------------------------------------------------------------------------------------------------
how many movies
ATTRIBUTES
----------------------------------------------------------------------------------------------------
{"response":"SELECT SUM(1) FROM \"ADB_USER\".\"MOVIES\"","feedback_type":"negative","sql_id":null,"sql_text":"select ai showsql how many movies","feedback_content":null}Esempio: fornire un feedback positivo
L'esempio riportato di seguito mostra di aver approvato l'accettazione e la conferma dell'istruzione SQL generata (feedback positivo) utilizzando feedback_type come positive.
In questo esempio, la query recupera sql_id dalla vista v$mapped_sql per il prompt specificato. Per ulteriori informazioni, vedere V_MAPPED_SQL.
Per aggiungere il feedback al profilo AI denominato OCI_FEEDBACK1, chiamare la procedura DBMS_CLOUD_AI.FEEDBACK con il parametro sql_id. Quindi, è possibile recuperare le colonne content e attributes dalla tabella <profile_name>_FEEDBACK_VECINDEX$VECTAB, che è collegata a tale query SQL specifica. Selezionare AI per creare automaticamente questa tabella vettoriale la prima volta che si utilizza la funzione di feedback. Per ulteriori informazioni, vedere Indice vettoriale per FEEDBACK.
SQL> select ai showsql how many distinct movie genres?;
RESPONSE
-----------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT g."GENRE_NAME") AS "Number of Movie Genres" FROM "ADB_USER"."GENRES" g
SQL> SELECT sql_id FROM v$mapped_sql WHERE sql_text = 'select ai showsql how many distinct movie genres?';
SQL_ID
-------------
852w8u83gktc1
SQL> exec DBMS_CLOUD_AI.FEEDBACK(profile_name=>'OCI_FEEDBACK1', sql_id=> '852w8u83gktc1', feedback_type=>'positive', operation=>'add');
PL/SQL procedure successfully completed.
SQL> SELECT content, attributes FROM OCI_FEEDBACK1_FEEDBACK_VECINDEX$VECTAB WHERE JSON_VALUE(attributes, '$.sql_id') ='852w8u83gktc1';
CONTENT
----------------------------------------------------------------------------------------------------
how many distinct movie genres?
ATTRIBUTES
----------------------------------------------------------------------------------------------------
{"response":"SELECT COUNT(DISTINCT g.\"GENRE_NAME\") AS \"Number of Movie Genres\" FROM \"ADB_USER\".\"GENRES\" g","feedback_type":"positive","sql_id":"852w8u83gktc1","sql_text":"select ai showsql how many distinct movie genres?","feedback_content":null}Esempio: aggiunta o eliminazione del feedback per l'istruzione SQL generata
L'esempio seguente illustra l'aggiunta o l'eliminazione del feedback per l'istruzione SQL generata specificando i parametri della procedura
DBMS_CLOUD_AI.FEEDBACK. In questo esempio viene illustrato come utilizzare sql_id e sql_text insieme ad altri parametri.
Nota
Selezionare AI consente una sola voce di feedback per ogni
Selezionare AI consente una sola voce di feedback per ogni
sql_id. Se si fornisce un feedback aggiuntivo per lo stesso sql_id, Select AI sostituisce la voce precedente con quella nuova.
Per ulteriori informazioni sui parametri, vedere Procedura FEEDBACK.
EXEC DBMS_CLOUD_AI.FEEDBACK(profile_name=>'OCI_FEEDBACK1',
sql_id=> '852w8u83gktc1',
feedback_type=>'positive',
operation=>'add');
EXEC DBMS_CLOUD_AI.FEEDBACK(profile_name=>'OCI_FEEDBACK1',
sql_text=> 'select ai showsql how many movies',
feedback_type=> 'negative',
response=>'SELECT SUM(1) FROM "ADB_USER"."MOVIES"',
feedback_content=>'Use SUM instead of COUNT');
EXEC DBMS_CLOUD_AI.FEEDBACK(profile_name=>'OCI_FEEDBACK1',
sql_id=> '852w8u83gktc1',
operation=>'delete');Esempio: utilizzare l'azione di feedback con l'ultima istruzione SQL AI per fornire feedback negativi
In questo esempio viene fornita una dimostrazione dell'uso dell'azione feedback per migliorare l'istruzione SQL generata suggerendo le modifiche utilizzando il linguaggio naturale.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name=>'OCI_FEEDBACK1',
attributes=>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"oci_compartment_id": "ocid1.compartment.oc1..aaaa...",
"object_list": [{"owner": "ADB_USER", "name": "users"},
{"owner": "ADB_USER", "name": "movies"},
{"owner": "ADB_USER", "name": "genres"},
{"owner": "ADB_USER", "name": "watch_history"},
{"owner": "ADB_USER", "name": "movie_genres"},
{"owner": "ADB_USER", "name": "employees1"},
{"owner": "ADB_USER", "name": "employees2"}
]
}');
END;
/
EXEC DBMS_CLOUD_AI.SET_PROFILE('OCI_FEEDBACK1');
PL/SQL procedure successfully completed.
select ai showsql rank movie duration;
RESPONSE
-------------------------------------------------------------------------------
SELECT "DURATION" AS "Movie Duration" FROM "ADB_USER"."MOVIES" ORDER BY "DURATION"
select ai feedback use ascending sorting;
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Based on your feedback, the SQL query for prompt "rank movie duration" is successfully refined. The refined SQL query as following:
SELECT m."DURATION" AS "Movie Duration" FROM "ADB_USER."MOVIES" m ORDER BY m."DURATION" ASC
select ai showsql rank the movie duration;
RESPONSE
-----------------------------------------------------------------------------------------
SELECT m."DURATION" AS "Movie Duration" FROM "ADB_USER."MOVIES" m ORDER BY m."DURATION" ASCEsempio: utilizzare l'azione di feedback con l'ultima istruzione SQL AI per fornire un feedback positivo
Questo esempio illustra l'uso dell'azione feedback per accettare l'istruzione SQL generata utilizzando il linguaggio naturale.
--Positive feedback
select ai showsql which movies are comedy?;
RESPONSE
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT DISTINCT m."TITLE" AS "Movie Title" FROM "ADB_USER"."MOVIES" m INNER JOIN "ADB_USER"."MOVIE_GENRES" mg ON m."MOVIE_ID" = mg."MOVIE_ID" INNER JOIN "ADB_USER"."GENRES" g ON mg."GENRE_ID" = g."GENRE_ID" WHERE g."GENRE_NAME" = 'comedy'
select ai feedback this is correct;
RESPONSE
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Thank you for your positive feedback. The SQL query for prompt "which movies are comedy?" is correctly implemented and delivering the expected results. It will be referenced for future optimizations and improvements.
Select AI Feedback Action Referring SQL_IDEsempio: utilizzare l'azione Feedback con SQL_ID per fornire feedback
In questo esempio viene illustrato come utilizzare SQL_ID con l'azione feedback per fornire un feedback per una determinata query SQL generata. È possibile ottenere SQL_ID eseguendo una query sulla tabella v$MAPPED_SQL.
-- Query mentioned with SQL_ID
select ai showsql how many movies are in each genre;
RESPONSE
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT g."GENRE_NAME" AS "Genre Name", COUNT(m."MOVIE_ID") AS "Number of Movies" FROM "ADB_USER"."MOVIES" m INNER JOIN "ADB_USER"."MOVIE_GENRES" mg ON m."MOVIE_ID" = mg."MOVIE_ID" INNER JOIN "ADB_USER"."GENRES" g ON mg."GENRE_ID" = g."GENRE_ID" GROUP BY g."GENRE_NAME"
select sql_id from v$cloud_ai_sql where sql_text = 'select ai showsql how many movies are in each genre';
SQL_ID
-------------
8azkwc0hr87ga
select ai feedback for query with sql_id = '8azkwc0hr87ga', rank in descending sorting;
RESPONSE
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Based on your feedback, the SQL query for prompt "how many movies are in each genre" is successfully refined. The refined SQL query as following:
SELECT g."GENRE_NAME" AS "Genre Name", COUNT(m."MOVIE_ID") AS "Number of Movies"
FROM "ADB_USER"."MOVIES" m
INNER JOIN "ADB_USER"."MOVIE_GENRES" mg ON m."MOVIE_ID" = mg."MOVIE_ID"
INNER JOIN "ADB_USER"."GENRES" g ON mg."GENRE_ID" = g."GENRE_ID"
GROUP BY g."GENRE_NAME"
ORDER BY COUNT(m."MOVIE_ID") DESC
Esempio: azione di feedback con testo query
Questo esempio mostra l'azione feedback per una specifica query Select AI includendo il prompt Select AI tra apici seguito dal feedback.
-Query mentioned with SQL_TEXT
select ai showsql how many watch history in total;
RESPONSE
----------------------------------------------------------------------------------
SELECT COUNT(w."WATCH_ID") AS "Total Watch History" FROM "ADB_USER"."WATCH_HISTORY" w
select ai feedback for query "select ai showsql how many watch history in total", name the column as total_watch;
RESPONSE
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Based on your feedback, the SQL query for prompt "how many watch history in total" is successfully refined. The refined SQL query as following:
SELECT COUNT(w."WATCH_ID") AS "total_watch" FROM "ADB_USER"."WATCH_HISTORY" wArgomento padre: Esempi di utilizzo di Select AI
Esempio: Seleziona sintetico AI
Questi esempi mostrano come utilizzare l'azione summarize e la funzione DBMS_CLOUD_AI.SUMMARIZE. Inoltre, personalizzare la generazione di riepilogo per il contenuto utilizzando la funzione.
Prima di iniziare
Rivedere Esegui prerequisiti per selezione AI.
Esempio: uso dell'azione di riepilogo sulla riga di comando SQL
Nell'esempio seguente viene utilizzato
SUMMARIZE come azione Select AI. Utilizzare SELECT AI SUMMARIZE <TEXT> nella riga di comando SQL per generare un riepilogo del testo di input.
SELECT AI SUMMARIZE
Like countless other people around the globe, I stream music, and like more
than six hundred million of them I mainly use Spotify. Streaming currently
accounts for about eighty per cent of the American recording industry’s
revenue, and in recent years Spotify’s health is often consulted as a measure
for the health of the music business over all. Last spring, the International
Federation of the Phonographic Industry reported global revenues of $28.6
billion, making for the ninth straight year of growth. All of this was
unimaginable in the two-thousands, when the major record labels appeared
poorly equipped to deal with piracy and the so-called death of physical media.
On the consumer side, the story looks even rosier. Adjusted for inflation, a
... (skipped 1000 rows in the middle)
Pelly writes of some artists, in search of viral fame, who surreptitiously use
social media to effectively beta test melodies and motifs, basically putting
together songs via crowdsourcing. Artists have always fretted about the
pressure to conform, but the data-driven, music-as-content era feels
different. “You are a Spotify employee at that point,” Daniel Lopatin, who
makes abstract electronic music as Oneohtrix Point Never, told Pelly. “If your
art practice is so ingrained in the brutal reality that Spotify has outlined
for all of us, then what is the music that you’re not making? What does the
music you’re not making sound like?” Listeners might wonder something similar.
What does the music we’re not hearing sound like?;
RESPONSE
------------------------------------------------------------------------------
The music streaming industry, led by Spotify, has revolutionized the way
people consume music, with streaming accounting for 80% of the American
recording industry's revenue. However, this shift has also complicated the
lives of artists, who struggle to survive in a hyper-abundant present where
music is often valued for its convenience rather than its artistic merit.
Spotify's algorithms prioritize popularity and profitability over artistic
diversity, leading to a homogenization of music and a devaluation of the
labor that goes into creating it. Meanwhile, the company's executives reap
enormous profits, with CEO Daniel Ek's net worth rivaling that of the
wealthiest musicians. As music critic Liz Pelly argues, the streaming economy
raises important questions about autonomy, creativity, and the value of art
in a world where everything is readily available and easily accessible.Suggerimento:
In SQL*Plus, una virgoletta singola (
') viene considerata come delimitatore di stringa. Se il testo contiene virgolette singole, eseguire l'escape raddoppiando il testo (da ' a '') oppure racchiudere il testo utilizzando il meccanismo di quotazione q'[]'. Se il testo contiene virgolette doppie vuote (""), racchiudere il testo utilizzando il meccanismo q'[]'. Ad esempio:SELECT AI SUMMARIZE q'[this's a text]';Esempio: utilizzare la procedura DBMS_CLOUD_AI.SUMMARIZE per generare un riepilogo
In questi esempi viene illustrato come generare un riepilogo utilizzando parametri diversi dalla procedura DBMS_CLOUD_AI.SUMMARIZE.
È possibile generare un riepilogo da più di 3000 parole di testo memorizzate in uno storage degli oggetti OCI specificando il collegamento allo storage degli oggetti come parametro
location_uri e le credenziali dell'account cloud come credential_name utilizzando DBMS_CLOUD_AI.SUMMARIZESELECT DBMS_CLOUD_AI.SUMMARIZE(
location_uri => 'https://objectstorage.ca-toronto-1.oraclecloud.com/n/' ||
'namespace-string/b/bucketname/o/data_folder/' ||
'summary/test_4000_words.txt',
credential_name => 'STORE_CRED',
profile_name => 'GENAI')
from DUAL;Un altro modo per generare un riepilogo da un testo memorizzato in uno storage degli oggetti OCI è utilizzare il parametro
content per chiamare la procedura DBMS_CLOUD.GET_OBJECT.
SELECT DBMS_CLOUD_AI.SUMMARIZE(
content => TO_CLOB(
DBMS_CLOUD.GET_OBJECT(
credential_name => 'STORE_CRED',
location_uri => 'https://objectstorage.ca-toronto-1.oraclecloud.com/n/' ||
'namespace-string/b/bucketname/o/data_folder/' ||
'summary/test_4000_words.txt')),
profile_name => 'GENAI'>)
from DUAL;Esempio: generare un riepilogo specificando prompt utente, numero minimo di parole e numero massimo di parole
L'esempio seguente illustra la generazione di un riepilogo di più di 3000 parole specificando i seguenti parametri:
user_prompt: il riepilogo deve iniziare con 'Il riepilogo dell'articolo è: 'min_words: 50max_words: 100
SELECT DBMS_CLOUD_AI.SUMMARIZE(
content => TO_CLOB(
DBMS_CLOUD.GET_OBJECT(
credential_name =>'STORE_CRED',
location_uri =>'https://objectstorage.ca-toronto-1.oraclecloud.com/n/' ||
'namespace-string/b/bucketname/o/data_folder/' ||
'summary/test_4000_words.txt')),
profile_name => 'GENAI',
user_prompt => 'The summary should start with ''The summary of ' ||
'the article is: ''',
params => '{"min_words":50,"max_words":100}')
As response FROM dual;
RESPONSE
--------------------------------------------------------------------------------
The summary of the article is: The music streaming industry, led by Spotify, has
revolutionized the way people consume music, with streaming accounting for abou
t eighty per cent of the American recording industry's revenue. However, this sh
ift has also raised concerns about the impact on artists, with many struggling t
o make a living due to low royalty rates and the dominance of playlists. The art
icle explores the history of music streaming, from the early days of Napster to
the current landscape, and how it has changed the way people listen to music. It
also delves into the issues of autonomy and creativity in the music industry, w
ith some artists feeling pressured to conform to certain styles or formulas to s
ucceed on platforms like Spotify. The article cites examples of artists who have
spoken out against the streaming economy, including Taylor Swift and Neil Young
, and discusses the rise of alternative platforms like Bandcamp and Nina. Ultima
tely, the article suggests that the streaming economy has created a perverse vis
ion for art, where music is valued for its ability to be ignored rather than app
reciated, and that this has significant implications for the future of music and
creativity. With the rise of AI-generated music and the increasing importance o
f data-driven decision making in the music industry, the article asks what the m
usic we're not hearing sounds like, and what the consequences of this shift will
be for artists and listeners alike. The article concludes by highlighting the n
eed for a more nuanced understanding of the music industry and the impact of str
eaming on artists and listeners, and for alternative models that prioritize crea
tivity and autonomy over profit and convenience.Esempio: generare un riepilogo specificando prompt utente, numero massimo di parole e stile di riepilogo
L'esempio seguente mostra la generazione di un riepilogo di più di 12000 parole specificando i seguenti parametri:
user_prompt: il riepilogo deve iniziare con 'Il riepilogo dell'articolo è: 'max_words: 100summary_style: list
SELECT DBMS_CLOUD_AI.SUMMARIZE(
location_uri => 'https://objectstorage.ca-toronto-1.' ||
'oraclecloud.com/n/namespace-string/b/' ||
'/bucketname/o/data_folder/' ||
'summary/dreams.txt',
credential_name => 'STORE_CRED',
profile_name => 'GENAI',
user_prompt => 'The summary should start with ''The summary of ' ||
'the article is: ''',
params => '{"max_words":100, "summary_style":"list"}')
As response FROM dual;
RESPONSE
--------------------------------------------------------------------------------
The summary of the article is:
- The book "Dreams" by Henri Bergson explores the concept of dreams and their si
gnificance in understanding human consciousness.
- Bergson argues that dreams are not just random thoughts, but rather a way for
our unconscious mind to process and consolidate memories.
- He suggests that dreams are a result of the relaxation of our mental faculties
, which allows our unconscious mind to freely associate and create new connectio
ns between memories.
- The book also discusses the role of sensations, such as visual and auditory im
pressions, in shaping our dreams.
- Bergson's theory of dreams is compared to other theories, including those of F
reud and Jung, and is seen as a unique and insightful contribution to the field
of psychology.
- The book concludes by highlighting the importance of studying dreams in order
to gain a deeper understanding of human consciousness and the workings of the mi
nd.Esempio: generazione di un riepilogo di un registro
In questo esempio viene illustrato come passare un file MiB 35.66 come input per generare un riepilogo. La funzione DBMS_CLOUD_AI.SUMMARIZE utilizza il metodo di perfezionamento iterativo per elaborare i chunk. Per ulteriori informazioni, vedere Perfezionamento iterativo.
SELECT DBMS_CLOUD_AI.SUMMARIZE(
location_uri => 'https://objectstorage.ca-toronto-1.oraclecloud.com/n/namespace-string/b/' ||
'bucketname/o/data_folder/summary/Descartes_An_Intellectual_Biography.pdf',
credential_name => 'STORE_CRED',
profile_name => 'GENAI',
params => '{"chunk_processing_method":"iterative_refinement"}')
AS response FROM dual;
RESPONSE
--------------------------------------------------------------------------------
Stephen Gaukroger's intellectual biography of Rene Descartes provides a detailed
examination of the philosopher's crucial role in shaping modern thought, placin
g him within the cultural, religious, and scientific context of the early sevent
eenth century. It traces Descartes' intellectual journey from his education at L
a Fleche, where he rejected Aristotelian logic, to his influential interactions
with figures like Isaac Beeckman, which shaped his mechanistic worldview evident
in works like his hydrostatics manuscript and *Compendium Musicae*. The biograp
hy underscores Descartes' dual commitment to philosophy and science, highlightin
g his social status among the gentry, mathematical innovations such as solving t
he Pappus problem through algebraic geometry, and his epistemology based on clea
r and distinct ideas. It explores his mechanistic explanations of bodily functio
ns, challenging traditional soul-body distinctions, and his extensive natural ph
ilosophy in texts like *Le Monde* and *L'Homme*. Gaukroger also delves into Desc
artes' cosmological theories, including the vortex theory and laws of motion lin
ked to divine immutability, as well as his nuanced perspectives on animal cognit
ion versus human consciousness. Central to the narrative is Descartes' use of hy
perbolic doubt to combat skepticism and establish metaphysical foundations throu
gh the *cogito*, alongside his classification of ideas and theological proofs of
God's existence. The complex relationship between his natural philosophy and me
taphysics, especially in defining motion as a mode, and his innovative approach
to the passions in *Passions of the Soul*, rejecting Stoic views for a mind-body
union, are key themes. This portrayal captures Descartes' struggle with traditi
onal paradigms during a transformative era, emphasizing his enduring impact on p
hilosophy and science.Argomento padre: Esempi di utilizzo di Select AI
Esempio: Seleziona traduzione AI
In questi esempi viene illustrato come utilizzare la funzionalità translate.
Prima di iniziare
Revisione:
Esempio: uso dell'azione di traduzione nella riga di comando SQL
L'esempio seguente mostra l'uso dell'azione translate nella riga di comando SQL.
Nota
Il profilo AI deve specificare la lingua di destinazione. Questa funzione è supportata solo per il provider OCI.
Il profilo AI deve specificare la lingua di destinazione. Questa funzione è supportata solo per il provider OCI.
--Create an AI profile with language parameters
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GENAI_NEW',
attributes =>'{"provider": "oci",
"credential_name": "GENAI_CRED",
"target_language": "french",
"object_list": [{"owner": "SH", "name": "customers"},
{"owner": "SH", "name": "countries"},
{"owner": "SH", "name": "supplementary_demographics"},
{"owner": "SH", "name": "profits"},
{"owner": "SH", "name": "promotions"},
{"owner": "SH", "name": "products"}]
}');
END;
/
PL/SQL procedure successfully completed.
SQL> exec DBMS_CLOUD_AI.SET_PROFILE('GENAI_NEW');
PL/SQL procedure successfully completed.
SQL> select ai translate I need to translate this;
RESPONSE
---------------------
Je dois traduire ceciEsempio: uso della funzione Traduci in DBMS_CLOUD_AI.GENERATE
Gli esempi riportati di seguito mostrano l'utilizzo di translate come azione Select AI all'interno della funzione DBMS_CLOUD_AI.GENERATE. Per ulteriori informazioni, vedere Funzione GENERATE.
Nota
Il profilo AI può saltare la specifica del parametro della lingua di destinazione se viene passato come attributo in
Il profilo AI può saltare la specifica del parametro della lingua di destinazione se viene passato come attributo in
DBMS_CLOUD_AI.GENERATE.
L'azione translate viene fornita nella funzione DBMS_CLOUD_AI.GENERATE insieme a target_language e source_language. In questo esempio viene utilizzata la traduzione dell'AI generativa. Il testo di input this is a document in inglese (source_language: "en") viene tradotto in francese (target_language: "fr").
SELECT DBMS_CLOUD_AI.GENERATE('select ai translate text to be translated')
FROM dual;
DECLARE
l_attributes clob := '{"target_language": "fr", "source_language": "en"}';
output clob;
BEGIN
output := DBMS_CLOUD_AI.GENERATE(
prompt => 'this is a document',
profile_name => 'oci_translate',
action => 'translate',
attributes => l_attributes
);
Esempio: utilizzare la funzione DBMS_CLOUD_AI.TRANSLATE per la traduzione
In questo esempio la funzione DBMS_CLOUD_AI.TRANSLATE viene chiamata per utilizzare la traduzione AI generativa, convertendo il testo di input dall'inglese (source_language) in francese (target_language) utilizzando il profilo AI specificato.
Per ulteriori informazioni, vedere Funzione TRANSLATE.
BEGIN
output_text := DBMS_CLOUD_AI.TRANSLATE(
profile_name => 'GENAI_NEW'
text => 'text to be translated',
source_language => 'English',
target_language => 'French');
END;
/Esempio: visualizzazione delle lingue supportate per un provider
Eseguire una query sulla vista AI_TRANSLATION_LANGUAGES per visualizzare un elenco di lingue supportate dal provider AI. Per i dettagli, vedere AI_TRANSLATION_LANGUAGES View.
SELECT* FROM AI_TRANSLATION_LANGUAGES;
LANGUAGE_NAME LANGUAGE_CODE PROVIDER
-------------------- -------------------- ---------------
ARABIC ar OCI
CROATIAN hr OCI
CZECH cs OCI
DANISH da OCI
GERMAN de OCI
GREEK el OCI
ENGLISH en OCI
SPANISH es OCI
FINNISH fi OCI
FRENCH fr OCI
FRENCH CANADA fr-CA OCI
HEBREW he OCI
HUNGARIAN hu OCI
ITALIAN it OCIEsempio: utilizzo di un profilo diverso per il linguaggio naturale per la generazione SQL
Questo esempio mostra l'impostazione di un profilo diverso denominato
openai con OpenAI come provider. È possibile utilizzare questo profilo per generare SQL tra le altre funzioni e azioni Select AI dei prompt.
Nota
Il profilo in questo esempio non supporta la funzionalità
Il profilo in questo esempio non supporta la funzionalità
translate.
-- OpenAI profile used for sql generation
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name = > 'openai',
attributes => '{"provider": "openai",
"credential_name": "OPENAI_CRED",
"object_list": [{"owner": "ADB_USER", "name": "GENRE"},
{"owner": "ADB_USER", "name": "CUSTOMER"},
{"owner": "ADB_USER", "name": "PIZZA_SHOP"},
{"owner": "ADB_USER", "name": "STREAMS"},
{"owner": "ADB_USER", "name": "MOVIES"},
{"owner": "ADB_USER", "name": "ACTIONS"}]
}');
END;
/
EXEC DBMS_CLOUD_AI.SET_PROFILE('openai');
SELECT AI SHOWSQL Give me the total number of customers;
RESPONSE
------------
SELECT COUNT("Customer_ID") AS "Total_Customers" FROM "Customers"Argomento padre: Esempi di utilizzo di Select AI
Esempio: limitazione dell'accesso alle tabelle nel profilo AI
In questo esempio viene illustrato come limitare l'accesso alle tabelle e come indicare all'LLM di utilizzare solo le tabelle specificate nel file object_list del profilo AI.
Impostare enforce_object_list su true per limitare l'accesso alle tabelle all'LLM.
Come utente del database, crea e configura il tuo profilo AI. Rivedere Esegui prerequisiti per Seleziona AI per configurare il profilo AI.
BEGIN
DBMS_CLOUD_AI.CREATE_PROFILE(
profile_name =>'GOOGLE_ENFORCED',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "GENRE"},
{"owner": "ADB_USER", "name": "CUSTOMER"},
{"owner": "ADB_USER", "name": "PIZZA_SHOP"},
{"owner": "ADB_USER", "name": "STREAMS"},
{"owner": "ADB_USER", "name": "MOVIES"},
{"owner": "ADB_USER", "name": "ACTIONS"}],
"enforce_object_list" : "true"
}');
END;
/
PL/SQL procedure successfully completed.
EXEC DBMS_CLOUD_AI.set_profile('GOOGLE_ENFORCED');
PL/SQL procedure successfully completed.
select ai showsql please list the user tables;
RESPONSE
--------------------------------------------------------------------------------------------
SELECT 'ADB_USER.GENRE' AS TABLE_NAME FROM DUAL UNION ALL SELECT 'ADB_USER.CUSTOMER' AS
TABLE_NAME FROM DUAL UNION ALL SELECT 'ADB_USER.PIZZA_SHOP' AS TABLE_NAME FROM DUAL UNION
ALL SELECT 'ADB_USER.STREAMS' AS TABLE_NAME FROM DUAL UNION ALL SELECT 'ADB_USER.MOVIES'
AS TABLE_NAME FROM DUAL
--L'impostazione di enforce_object_list su false indica all'LLM di utilizzare altre tabelle e viste in base alle relative conoscenze precedenti.
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
Argomento padre: Esempi di utilizzo di Select AI
Esempio: Specifica distinzione tra maiuscole e minuscole per le colonne
In questo esempio viene illustrato come impostare la distinzione tra maiuscole e minuscole per le colonne nel profilo AI.
Impostare case_sensitive_values su false per recuperare le query che non fanno distinzione tra maiuscole e minuscole.
Come utente del database, crea e configura il tuo profilo AI. Rivedere Esegui prerequisiti per Seleziona AI per configurare il profilo AI.
BEGIN
DBMS_CLOUD_AI.create_profile(
profile_name =>'GOOGLE',
attributes =>'{"provider": "google",
"credential_name": "GOOGLE_CRED",
"object_list": [{"owner": "ADB_USER", "name": "GENRE"},
{"owner": "ADB_USER", "name": "CUSTOMER"},
{"owner": "ADB_USER", "name": "PIZZA_SHOP"},
{"owner": "ADB_USER", "name": "STREAMS"},
{"owner": "ADB_USER", "name": "MOVIES"},
{"owner": "ADB_USER", "name": "ACTIONS"}],
"case_sensitive_values" : "false"
}');
END;
/
PL/SQL procedure successfully completed.
-- With "case_sensitive_values" set to "false", LLM will give back case insensitive query.
select ai showsql how many people watch Inception;
RESPONSE
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c.CUSTOMER_ID) AS "COUNT"
FROM "ADB_USER"."CUSTOMER" c
JOIN "ADB_USER"."STREAMS" s ON c.CUSTOMER_ID = s.CUSTOMER_ID
JOIN "ADB_USER"."MOVIES" m ON s.MOVIE_ID = m.MOVIE_ID
WHERE UPPER(m.TITLE) = UPPER('Inception')È possibile specificare una query con distinzione tra maiuscole e minuscole utilizzando le virgolette doppie anche se case_sensitive_values è impostato su false.
select ai showsql how many people watch "Inception";
RESPONSE
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
SELECT COUNT(DISTINCT c.CUSTOMER_ID) AS "COUNT"
FROM "ADB_USER"."CUSTOMER" c JOIN "ADB_USER"."STREAMS" s ON
c.CUSTOMER_ID = s.CUSTOMER_ID JOIN "ADB_USER"."MOVIES" m ON
s.MOVIE_ID = m.MOVIE_ID WHERE m.TITLE = 'Inception'
Argomento padre: Esempi di utilizzo di Select AI