This directory contains samples for python-select-ai. To run the scripts, define and export the following environment variables

export SELECT_AI_ADMIN_USER=<db_admin>
export SELECT_AI_ADMIN_PASSWORD=<db_admin_password>
export SELECT_AI_USER=<select_ai_db_user>
export SELECT_AI_PASSWORD=<select_ai_db_password>
export SELECT_AI_DB_CONNECT_STRING=<db_connect_string>
export TNS_ADMIN=<path/to/dir_containing_tnsnames.ora>

Note: In production, do not save secrets in environment variables

SELECT_AI_ADMIN_USER and SELECT_AI_ADMIN_PASSWORD are needed only to grant privileges to regular user. They are used in 2 sample scripts enable_ai_provider.py and disable_ai_provider.py

Some of the new samples use this optional environment variable:

• SELECT_AI_PROFILE_NAME — existing profile for the conversation and supervised-team, profile lifecycle, translation, and request-attribute samples.
• SELECT_AI_SHARE_GRANTEE — database user or role used by the sharing sample.
• SELECT_AI_OWNER, SELECT_AI_VECTOR_INDEX_NAME, SELECT_AI_TEAM_NAME, and SELECT_AI_CREDENTIAL_NAME — optional names used by the sharing sample.

Create a profile and run a test chat against one of the supported cloud AI providers:

export AWS_ACCESS_KEY_ID=<aws_access_key>
export AWS_SECRET_ACCESS_KEY=<aws_secret_key>
python samples/profile_create_aws.py

export AZURE_API_KEY=<azure_api_key>
python samples/profile_create_azure.py

export GOOGLE_API_KEY=<google_api_key>
python samples/profile_create_gcp.py

The scripts create or replace the provider credential and profile, grant the database user's HTTP access to the provider endpoint, and print a test chat response. See the provider documentation for the provider settings and representative output.

Create a team with a dedicated supervisor agent, run a prompt through the supervisor workflow, and inspect the database-generated supervisor task:

python samples/agent/team_supervisor_inspect.py
python samples/agent/async/team_supervisor_inspect.py

The samples set AgentAttributes(supervisor=True) on the coordinating agent and pass that agent's name as TeamAttributes.supervisor_agent. The database populates supervisor_task when the team is created.

Retrieve a canonical database definition:

python samples/agent/get_definition.py
python samples/agent/async/get_definition.py

Inspect and directly invoke a PL/SQL tool:

python samples/agent/tool_run_describe.py
python samples/agent/async/tool_run_describe.py

The supervised-team samples above also demonstrate Team.describe_team()/AsyncTeam.describe_team() and Team.list_tools()/AsyncTeam.list_tools().

Inspect the latest team execution and its related task and tool history:

python samples/agent/history_list.py
python samples/agent/async/agent_history_list.py

The async sample uses AsyncTeamHistory, AsyncTaskHistory, and AsyncToolHistory with async iteration.

Disable and re-enable an existing profile without deleting it:

python samples/profile_enable_disable.py
python samples/async/profile_enable_disable.py

The scripts use SELECT_AI_PROFILE_NAME when set; otherwise they use the sample profile name oci_ai_profile.

Translate text while letting the provider detect the source language:

python samples/profile_translate.py
python samples/async/profile_translate.py

These samples use SELECT_AI_PROFILE_NAME when set and pass only the target language at the call site. The default profile names are oci_ai_profile for the synchronous sample and async_oci_ai_profile for the asynchronous sample. Profile-level language defaults can be configured with ProfileAttributes when the target is also omitted.

Override profile attributes for one request without changing the saved profile:

python samples/profile_request_attributes.py
python samples/async/profile_request_attributes.py

The samples demonstrate additional_instructions, integer seed, and source and target language settings passed through the attributes mapping.

Inspect owner-qualified profiles and vector indexes and grant/revoke access to profiles, vector indexes, teams, and credentials:

python samples/sharing.py
python samples/async/sharing.py

Set SELECT_AI_SHARE_GRANTEE before running the scripts. The objects must already exist, and the scripts should run as their owner. The credential creation and deletion samples also create and remove a public synonym.

Create a conversation, list its stored prompts, delete a prompt, and manage conversation tags:

python samples/conversation_prompts_tags.py
python samples/async/conversation_prompts_tags.py

The scripts use SELECT_AI_PROFILE_NAME when set; otherwise they use the sample profile name oci_ai_profile. See the conversation user guide for representative output.

Start a Select AI A2A server before running these samples:

select-ai a2a serve --team ORACLE_AI_DATABASE_AGENT --port 8000

After starting a local A2A server, run the fixed sales-analysis prompt as a non-blocking task and poll it until completion:

python samples/a2a/task_poll.py

The sample sends the A2A v0.3 message/send request with configuration.blocking: false, prints the returned task ID, and polls tasks/get. Edit ENDPOINT or PROMPT at the top of the script if needed.

Representative output is:

Task 42b...: submitted
Task 42b...: working
Task 42b...: completed
{
  "id": "42b...",
  "status": {"state": "completed", "timestamp": "..."},
  "artifacts": [{"name": "database-agent-result", "parts": ["..."]}]
}

Task IDs, timestamps, and database answers vary between runs.

To compare it with the default blocking behavior, run:

python samples/a2a/blocking_task.py

This sample intentionally omits configuration.blocking. The server waits for the database work to finish and returns the completed Task in the initial message/send response; no polling is needed.

Representative output is:

Task 7e1...: completed
{
  "id": "7e1...",
  "status": {"state": "completed", "timestamp": "..."},
  "artifacts": [{"name": "database-agent-result", "parts": ["..."]}]
}

The dynamic session samples submit the A2UI database connection form, open a temporary database session, and execute database tasks. The server advertises streaming: false and supports non-blocking task execution with configuration.blocking: false and tasks/get.

Dynamic-session samples that perform the form handshake and then execute a real database task are in a2a/dynamic:

python samples/a2a/dynamic/blocking_task.py
python samples/a2a/dynamic/task_poll.py

See that README for standalone and clustered startup instructions.

The full A2A architecture, protocol details, session lifecycle, and Google Cloud deployment explanation are in the A2A user guide.

SELECT_AI_DB_CONNECT_STRING can be in any one of the following formats

• TNS alias

  export SELECT_AI_DB_CONNECT_STRING=db2025adb_medium

  Ensure there is an entry in $TNS_ADMIN/tnsnames.ora mapping to the connect descriptor

  >> tnsnames.ora
  
  db2025adb_medium = (description= (retry_count=20)(retry_delay=3)
                     (address=(protocol=tcps)(port=1521)(host=adb.<region>.oraclecloud.com))
                     (connect_data=(service_name=db2025adb_medium.adb.oraclecloud.com))
                     (security=(ssl_server_dn_match=yes)))

• Complete connect string

  export SELECT_AI_DB_CONNECT_STRING="(description= (retry_count=20)(retry_delay=3)
  (address=(protocol=tcps)(port=1521)(host=adb.<region>.oraclecloud.com))
  (connect_data=(service_name=db2025adb_medium.adb.oraclecloud.com))
  (security=(ssl_server_dn_match=yes)))"
  

• Simplified connect string

  export SELECT_AI_DB_CONNECT_STRING="tcps://adb.<region>.oraclecloud.com:1521/db2025adb_medium.adb.oraclecloud.com?retry_count=2&retry_delay=3"