GCP Oracle AI Database agent

autonomous-ai-agents/nl2sql_data_retrieval/nl2sql_data_retrieval_agent.sql

@@ -208,7 +208,7 @@ BEGIN
         'invoke GET_URL_CONTENT to validate the source content. ' ||
         'Always present answers in a clearly formatted and readable manner using bullet points. ' ||
         'At the end of the response, add a blank line followed by a **Sources** section. ' ||
-        'If SQL_TOOL was used, include the source tag * DATABASE. ' ||
+        'If SQL_TOOL was used, include the source tag * ORACLE AI DATABASE. ' ||
         'If WEBSEARCH was used, include a * WEBSEARCH section followed by a markdown list of referenced URLs. ' ||
         'If both were used, include both source sections. ' ||
         'Use {current_location} to identify the user location when required. ' ||

google-gemini-marketplace-agents/README.md

@@ -0,0 +1,238 @@
+# Oracle AI Database Agent
+
+## Overview
+
+The **Oracle AI Database Agent** enables natural-language data analysis workflows by combining NL2SQL generation, metadata inspection, query correction, and chart generation inside Oracle Autonomous AI Database.
+
+For definitions of **Tool**, **Task**, **Agent**, and **Agent Team**, see the top-level guide: [README](../README.md#simple-agent-execution-flow).
+
+## How the NL2SQL agent improves upon Select AI NL2SQL
+
+Oracle Select AI already provides Natural Language to SQL (NL2SQL), but **real-world data retrieval often fails** due to:
+
+- Ambiguous column values  
+- Unknown or incorrect value ranges (dates, numbers)  
+- Invalid predicates leading to zero-row results  
+- Lack of visualization support  
+
+This **Oracle AI Database Agent** addresses these limitations by combining:
+
+- Database introspection  
+- Fail-safe retries  
+- Distinct and range value discovery  
+- Chart and visualization generation  
+
+into a **single autonomous agent workflow**.
+
+---
+
+##  Architecture Overview
+
+```text
+User Query
+   ↓
+NL2SQL Task
+   ↓
+Agent Reasoning
+   ├── SQL_TOOL
+   ├── DISTINCT_VALUES_CHECK
+   ├── RANGE_VALUES_CHECK
+   └── GENERATE_CHART
+   ↓
+Final Verified Answer + Sources
+```
+
+The agent dynamically selects tools, retries intelligently, and produces **explainable outputs**.
+
+---
+
+##  Repository Contents
+
+```text
+.
+├── oracle_ai_database_agent_tool.sql
+│   ├── PL/SQL utility functions
+│   ├── Database-native analysis helpers
+│   └── AI tool registration
+│
+├── oracle_ai_database_agent.sql
+│   ├── Task definition
+│   ├── Agent creation
+│   ├── Team orchestration
+│   └── AI profile binding
+│
+└── README.md
+```
+
+---
+
+##  Prerequisites
+
+- Oracle Autonomous AI Database (26ai recommended)
+- Select AI enabled
+- Run as ADMIN 
+
+---
+
+##  Installation – Tools
+
+Before running installation commands:
+
+1. Clone or download this repository.
+2. Open a terminal and change directory to `google-gemini-marketplace-agents`.
+3. Choose one execution mode:
+   - SQL*Plus/SQLcl: run script files directly with `@script_name`.
+   - SQL Worksheet (Database Actions or other SQL IDE): open the `.sql` file and run/paste its contents.
+4. Uploading scripts to `DATA_PUMP_DIR` is not required for these methods.
+
+Run as `ADMIN` (or another privileged user):
+
+```sql
+sqlplus admin@<adb_connect_string> @oracle_ai_database_agent_tool.sql
+```
+### Input Parameters required to run
+- Target schema name (Schema where to the agent team needs to be installed)
+
+### What This Script Does
+
+- Grants required Select AI privileges  
+- Creates `SELECTAI_AGENT_CONFIG`  
+- Installs `ORACLE_AI_DATA_RETRIEVAL_FUNCTIONS`  
+- Registers all AI agent tools.
+
+---
+
+##  Installed Tools Explained
+
+### 1️⃣ SQL_TOOL
+**Purpose:** Generate SQL from natural language and run it safely.
+
+**Fail-safe behavior:**
+- SQL generation failure → feedback returned to the LLM  
+- Zero rows → agent retries using range or distinct tools  
+
+---
+
+### 2️⃣ DISTINCT_VALUES_CHECK
+**Purpose:** Discover valid categorical values before filtering.
+
+**Use cases:**
+- Status columns  
+- Country or region names  
+- Product categories  
+
+**Matching modes:** `fuzzy` (default), `exact`, `regex`
+
+---
+
+### 3️⃣ RANGE_VALUES_CHECK
+**Purpose:** Determine minimum and maximum values for numeric, DATE, or TIMESTAMP columns.
+
+**Use cases:**
+- Time-series analysis  
+- Salary or revenue ranges  
+- Boundary detection  
+
+---
+
+### 4️⃣ GENERATE_CHART
+**Purpose:** Generate **Chart.js-compatible JSON** for visualizations.
+
+**Supported chart types:**
+- bar, line, pie, doughnut  
+- radar, scatter, bubble, polarArea  
+
+---
+
+##  Installation – Agent and Team
+
+From `google-gemini-marketplace-agents`, run:
+
+```sql
+sqlplus admin@<adb_connect_string> @oracle_ai_database_agent.sql
+```
+
+You can also execute the contents of `oracle_ai_database_agent.sql` in SQL Worksheet.
+
+### Input Parameters required to run.
+- Target schema name (Schema where to the agent team needs to be installed)
+- AI Profile name (Select AI Profile name that needs to be used with the Agent)
+
+
+### Objects Created
+
+| Object | Name |
+|-------|------|
+| Task  | ORACLE_AI_DATABASE_TASK  |
+| Agent | ORACLE_AI_DATABASE_AGENT |
+| Team  | ORACLE_AI_DATABASE_TEAM  |
+
+---
+
+##  Task Intelligence Highlights
+
+The task definition enforces:
+- Tool-based reasoning  
+- Mandatory source attribution  
+- Structured, readable responses  
+- Explicit chart generation flow  
+- Retry logic for SQL failures  
+- Metadata-aware querying  
+
+---
+
+##  Generalizing Teams Using Tools
+
+### Recommended Design Pattern
+**Keep tools generic.  
+Specialize agents using tasks.**
+
+### Example Team Strategies
+
+| Team | Tools Used | Purpose |
+|----|-----------|--------|
+| Data Retrieval Team | All tools | General analytics |
+| Finance Analytics Team | SQL + RANGE | Financial reporting |
+| Visualization Team | SQL + CHART | Dashboards and insights |
+
+### Why This Scales Well
+- Tools are reusable  
+- Tasks define behavior  
+- Agents bind AI profiles  
+- Teams orchestrate workflows  
+
+### Example prompts
+After creating the Oracle AI Database Agent team, you can interact with it using prompts such as:
+
+- “How can you help?”
+- Ask questions related to the database tables associated with the selected profile.
+- To generate visualizations, explicitly mention the chart type, for example:
+  “Generate a bar chart for the result.” (any supported chart type can be used)
+
+---
+
+##  Best Practices
+
+- Use `DISTINCT_VALUES_CHECK` before filtering text columns  
+- Use `RANGE_VALUES_CHECK` for DATE and NUMBER columns    
+- Maintain separate AI profiles per environment   
+
+---
+
+##  License
+
+Universal Permissive License (UPL) 1.0  
+https://oss.oracle.com/licenses/upl/
+Copyright (c) 2026 Oracle and/or its affiliates.
+
+---
+
+## ✨ Final Thoughts
+
+This Oracle AI Database Agent elevates Select AI from a **SQL generator** to a **true autonomous data analyst** — capable of reasoning, validating, retrying, and visualizing data with confidence.
+
+Designed for:
+- Domain-specific agents  
+- Multi-team orchestration  
+- UI / APEX integrations  
+- Autonomous dashboards