feat: add LLM DB Query AI usecase (#876)

Author: gooishin

docs/use-cases.md

@@ -126,3 +126,4 @@ Transform your Intel® hardware into powerful AI and edge computing solutions wi
 9. [GMSL Camera Enablement](../usecases/camera/gmsl/README.md)
 10. [MIPI Camera Enablement](../usecases/camera/mipi/README.md)
 11. [Visual & Textual Query-driven Document Reasoning Engine](../usecases/ai/visual-textual-query-driven-document-reasoning-engine/README.md)
+12. [LLM Database Query using Intel AI Assistant Builder](../usecases/ai/llm_dbquery/README.md)

usecases/ai/llm_dbquery/.gitignore

@@ -0,0 +1,50 @@
+# .gitignore for ODBC SuperBuilder
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+venv/
+ENV/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+ENV/
+env/
+.venv/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Docker volumes (data persists in named volumes)
+databases/
+database_exports/
+
+# Logs
+*.log

usecases/ai/llm_dbquery/.primary_db_config

@@ -0,0 +1 @@
+production

usecases/ai/llm_dbquery/README.md

@@ -0,0 +1,89 @@
+<!--
+Copyright (C) 2025 Intel Corporation
+SPDX-License-Identifier: Apache-2.0
+-->
+
+# LLM DB Query (ODBC SuperBuilder)
+
+Lightweight toolkit for LLM-driven natural-language data exploration across multiple local SQLite databases using the MCP toolchain.
+Natural-language SQL queries across many local .sqlite files, with optional AI analysis of results.
+
+## Key features
+- Query multiple local `.sqlite` files from a single natural-language prompt
+- Convert NL → SQL via a local OpenAI*-compatible or llama.cpp endpoint
+- Execute SQL across attached databases using ODBC (pyodbc)
+- Chain SQL results into an MCP data analysis server for AI-powered reports
+- Export database schema and data to text files for verification and auditing
+
+## Architecture
+![Architecture diagram](./docs/Architecture.png)
+
+## Example (Increased Playback Speed)
+<img src="./docs/LLMDB.gif" alt="Example" width="800" />
+
+## Validated hardware
+- CPU: Intel Core Ultra Series 1 and above
+- GPU: Intel® Arc™ B60 graphics (serve 2 MCP and Intel Superbuilder in 1 machine)
+- RAM: 64GB
+- DISK: 256GB
+
+## Software requirements
+- Python 3.10+ (see requirements.txt)
+- ODBC driver appropriate for your platform
+- Windows 11 supported; test on the platform matching your environment
+
+## Quick start (Windows PowerShell)
+1. Install Python dependencies:
+```cmd
+pip install -r requirements.txt
+```
+
+2. (Optional) Create sample databases and generated exports:
+```cmd
+python create_databases.py
+```
+
+3. Run preflight checks:
+```cmd
+python startup_check.py
+```
+
+4. Start MCP servers (two terminals)
+- Database query server:
+```powershell
+cd ./mcp_odbcserver
+run.ps1
+```
+
+- Data analysis server:
+```powershell
+cd ./mcp_data_analysis_server
+run.ps1
+```
+
+5. From your MCP client (for example, Intel® AI Superbuilder):
+- Configure the MCP servers/agents for ODBC querying and data analysis
+- Send natural-language queries to the ODBC server; receive JSON results and analysis from the data-analysis server
+- Use System Prompt at Intel AI Assistant Builder as below:
+```
+# Data Assistant  Tools: query_database, analyze_data (both are MCP tools)  ## Workflow  For every user question:  **STEP 1: Execute Task 1** Call `query_database("user's question")` - Returns JSON: `{"data": [...], "reasoning": {...}}`  Show: - 🧠 Model Reasoning: [reasoning.model_reasoning] - 🔍 SQL: [reasoning.generated_sql] - 📊 Data: [display as table] - 🎯 Sources: [reasoning.databases_queried]  **STEP 2: Execute Task 2** Call `analyze_data(data=result["data"], analysis_question="user's question")`  **If analyze_data fails:** - Display the error message - Stop the workflow (do not retry) - Ask user if they want to proceed with just the query results  ## Rules ✅ Pass result["data"] as JSON to analyze_data ✅ Stop immediately if analyze_data throws an error ✅ Do not attempt to retry analyze_data on failure ✅ Complete all steps only if both succeed
+```
+
+## Project layout
+```
+usecases/ai/llm_dbquery/
+├── create_databases.py        # create sample DBs and exports
+├── query_databases.py         # ODBC helpers & multi-db logic
+├── nl_query.py                # NL -> SQL interface (schema discovery)
+├── startup_check.py           # preflight checks (drivers, LLM, deps)
+├── requirements.txt
+├── /databases                 # place your .sqlite files here
+├── /database_exports          # generated text exports
+├── /mcp_odbcserver            # ODBC server (MCP)
+└── /mcp_data_analysis_server  # data analysis server (MCP)
+```
+
+## FAQ
+Q: Where do I put my SQLite files?  
+A: Add .sqlite files to the `databases/` folder. The toolkit auto-discovers all `.sqlite` files (no hardcoded names) but make sure to select your primary database when you run `startup_check.py`
+