### Complete MAESTRO CLI Workflow Example

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

A step-by-step example demonstrating the setup of a user, creation of a document group, ingestion of documents, status check, and document search using the MAESTRO CLI.

```bash
# 1. Create a user
docker compose --profile cli run --rm cli python cli_ingest.py create-user researcher mypassword --full-name "Research User"

# 2. Create a document group
docker compose --profile cli run --rm cli python cli_ingest.py create-group researcher "AI Papers" --description "Artificial Intelligence Research Papers"

# 3. List groups to get the group ID
docker compose --profile cli run --rm cli python cli_ingest.py list-groups --user researcher

# 4. Copy PDFs to the pdfs directory
cp /path/to/your/papers/*.pdf ./pdfs/

# 5. Ingest documents (replace GROUP_ID with actual ID from step 3)
docker compose --profile cli run --rm cli python cli_ingest.py ingest researcher GROUP_ID /app/pdfs

# 6. Check processing status
docker compose --profile cli run --rm cli python cli_ingest.py status --user researcher

# 7. Search documents once processing is complete
docker compose --profile cli run --rm cli python cli_ingest.py search researcher "neural networks"
```

--------------------------------

### MAESTRO Web Interface Usage

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Instructions for starting the MAESTRO application and accessing its web interface.

```bash
docker compose up
```

--------------------------------

### Start MAESTRO with Docker Compose

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Builds the Docker images and starts the MAESTRO web application using Docker Compose. The application will be accessible via `http://localhost:3030`.

```bash
docker compose up
```

--------------------------------

### MAESTRO CLI Helper Script Usage

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Examples of using the `maestro-cli.sh` helper script for common MAESTRO operations. This includes making the script executable, showing help, creating users, creating groups, ingesting documents, and checking status.

```bash
# Make the script executable (first time only)
chmod +x maestro-cli.sh

# Show available commands
./maestro-cli.sh help

# Example commands
./maestro-cli.sh create-user researcher mypass123 --full-name "Research User"
./maestro-cli.sh create-group researcher "AI Papers" --description "Machine Learning Research"
./maestro-cli.sh ingest researcher ./pdfs --group GROUP_ID
./maestro-cli.sh status --user researcher
```

--------------------------------

### MAESTRO CLI: Get Help

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Commands to retrieve help information for the MAESTRO CLI and its subcommands.

```bash
docker compose --profile cli run --rm cli python cli_ingest.py --help
```

```bash
docker compose --profile cli run --rm cli python cli_ingest.py ingest --help
```

```bash
docker compose --profile cli run --rm cli python cli_ingest.py create-user --help
```

--------------------------------

### Environment Variable Setup

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Provides methods for setting up environment variables for MAESTRO. The `setup-env.sh` script offers a quick, guided setup, while manual copying and editing of `.env.example` allows for custom configurations. A legacy configuration path is also mentioned.

```bash
./setup-env.sh
```

```bash
cp .env.example .env
nano .env  # Edit with your API keys and network settings
```

```bash
cp maestro_backend/ai_researcher/.env.example maestro_backend/ai_researcher/.env
```

--------------------------------

### Troubleshooting: Container Won't Start

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Commands to diagnose and resolve issues when MAESTRO containers fail to start, including checking logs, verifying environment variables, and rebuilding images.

```bash
docker compose logs backend
```

```bash
docker compose logs frontend
```

```bash
docker compose logs doc-processor
```

```bash
docker compose build --no-cache
```

--------------------------------

### Network Configuration for Deployment Scenarios

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Provides example `.env` configurations for different deployment scenarios: Local Development, Production (Same Server), and Distributed Deployment. These examples highlight how to set `BACKEND_HOST`, `FRONTEND_HOST`, `API_PROTOCOL`, and `WS_PROTOCOL`.

```env
# Local Development:
BACKEND_HOST=localhost
FRONTEND_HOST=localhost
API_PROTOCOL=http
WS_PROTOCOL=ws
```

```env
# Production (Same Server):
BACKEND_HOST=0.0.0.0
FRONTEND_HOST=0.0.0.0
API_PROTOCOL=https
WS_PROTOCOL=wss
```

```env
# Distributed Deployment:
# Backend server .env
BACKEND_HOST=0.0.0.0
BACKEND_PORT=8001

# Frontend server .env  
BACKEND_HOST=api.yourdomain.com
FRONTEND_HOST=0.0.0.0
API_PROTOCOL=https
WS_PROTOCOL=wss
```

--------------------------------

### Advanced Configuration: Resource Limits

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Example of how to add resource limits (CPU and memory) for services in the `docker-compose.yml` file.

```yaml
services:
  backend:
    # ... existing configuration ...
    deploy:
      resources:
        limits:
          cpus: '4'
          memory: 8G
```

--------------------------------

### Example .env Configuration for Network Settings

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Illustrates a typical `.env` file configuration for MAESTRO, defining host and port settings for backend and frontend services, as well as protocol choices. These settings are used to dynamically construct API URLs.

```env
BACKEND_HOST=localhost          # Where backend runs
BACKEND_PORT=8001              # Backend port
FRONTEND_HOST=localhost        # Where frontend runs  
FRONTEND_PORT=3030            # Frontend port
API_PROTOCOL=http             # http or https
WS_PROTOCOL=ws               # ws or wss

# Automatically constructs:
# VITE_API_HTTP_URL=http://localhost:8001
# VITE_API_WS_URL=ws://localhost:8001
```

--------------------------------

### Clone Repository and Navigate

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Clones the MAESTRO repository and changes the directory to the project root. This is the first step in setting up MAESTRO locally.

```bash
git clone <repository-url>
cd researcher2
```

--------------------------------

### MAESTRO AI Configuration

Source: https://github.com/murtaza-nasir/maestro/blob/main/SETUP_GUIDE.md

Configuration options for Large Language Models (LLMs) in MAESTRO. Supports both basic unified provider setup and advanced per-agent configuration.

```APIDOC
AI Config:
  Basic Mode:
    AI Provider: [OpenRouter|OpenAI|Groq|...]
      - Select preferred LLM service provider.
    API Key: string
      - API key for the selected provider.
    Base URL: string
      - API endpoint URL (often pre-filled).
    Test Button: 
      - Verifies connection to the provider.
    Model Selection:
      Fast Model: string
        - Model for rapid, simple tasks.
      Mid Model: string
        - Model for balanced performance.
      Intelligent Model: string
        - Model for complex analysis.
      Verifier Model: string
        - Model for verification tasks.

  Advanced Mode:
    Enable Switch: boolean
      - Toggles advanced per-agent configuration.
    Fast Model Configuration:
      Provider: string
      Model Name: string
      API Key: string
      Base URL: string
    Mid Model Configuration:
      Provider: string
      Model Name: string
      API Key: string
      Base URL: string
    Intelligent Model Configuration:
      Provider: string
      Model Name: string
      API Key: string
      Base URL: string
    Verifier Model Configuration:
      Provider: string
      Model Name: string
      API Key: string
      Base URL: string

  Usage:
    - Basic mode is simpler for unified setup.
    - Advanced mode allows fine-tuning for cost and performance with different providers/models per agent type.
```

--------------------------------

### Troubleshooting: GPU Issues

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Steps to verify and troubleshoot GPU-related issues, including checking NVIDIA drivers and the NVIDIA Container Toolkit.

```bash
nvidia-smi
```

```bash
sudo docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
```

--------------------------------

### Troubleshooting: Permission Issues

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Command to resolve permission issues with mounted volumes by changing ownership.

```bash
sudo chown -R $(id -u):$(id -g) ./reports ./pdfs
```

--------------------------------

### MAESTRO Admin System Configuration

Source: https://github.com/murtaza-nasir/maestro/blob/main/SETUP_GUIDE.md

System-level configuration options for administrators, including user registration settings.

```APIDOC
Admin System Configuration:
  Enable New User Registration: boolean
    - Controls whether new users can self-register.
    - If disabled, only admins can create accounts.
```

--------------------------------

### Troubleshooting: CLI Issues

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Steps to troubleshoot MAESTRO CLI command failures, including ensuring the backend service is running, checking database accessibility, and verifying file permissions.

```bash
docker compose up -d backend
```

```bash
docker compose --profile cli run --rm cli python cli_ingest.py list-users
```

```bash
ls -la ./pdfs
```

--------------------------------

### MAESTRO Research Parameters

Source: https://github.com/murtaza-nasir/maestro/blob/main/SETUP_GUIDE.md

Fine-tuning parameters for the research process, which can be overridden for individual missions.

```APIDOC
Research Parameters:
  AI-Powered Configuration: boolean
    - Enables AI agent to dynamically optimize research parameters.
  Research Configuration:
    Max Depth: integer
      - Depth of the research outline.
    Max Questions: integer
      - Number of questions to generate for the research plan.
    Research Rounds: integer
      - Number of iterative research cycles.
    Writing Passes: integer
      - Number of passes for the writing agent to refine the report.
  Search Results:
    Initial Docs / Initial Web: integer
      - Number of results fetched during initial exploration.
    Main Docs / Main Web: integer
      - Number of results fetched during the main research phase.
  Performance & Options:
    Context Limit: integer
      - Number of recent thoughts for agent context.
    Max Notes: integer
      - Number of notes generated for reranking.
    Concurrent Requests: integer
      - Number of parallel operations.
```

--------------------------------

### MAESTRO Search Configuration

Source: https://github.com/murtaza-nasir/maestro/blob/main/SETUP_GUIDE.md

Configuration for the web search provider used by the Research Agent.

```APIDOC
Search Configuration:
  Search Provider: [Tavily|LinkUp|SearXNG|...]
    - Select preferred web search service.
    - SearXNG is for self-hosted search.
  API Key: string
    - API key for the selected search provider.
```

--------------------------------

### MAESTRO CLI User Management

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Commands for managing users within the MAESTRO system via the CLI. Includes creating new users with optional admin privileges and listing all users.

```python
docker compose --profile cli run --rm cli python cli_ingest.py create-user myusername mypassword --full-name "My Full Name"
```

```python
docker compose --profile cli run --rm cli python cli_ingest.py create-user admin adminpass --admin --full-name "Administrator"
```

```python
docker compose --profile cli run --rm cli python cli_ingest.py list-users
```

--------------------------------

### Managing MAESTRO Model Cache

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Bash commands for managing the MAESTRO model cache. Includes viewing cache size, clearing the cache, and backing up the cache for offline deployments.

```bash
du -sh maestro_model_cache maestro_datalab_cache
```

```bash
rm -rf maestro_model_cache maestro_datalab_cache
docker compose down
docker compose up --build
```

```bash
tar -czf maestro-models-cache.tar.gz maestro_model_cache maestro_datalab_cache
```

--------------------------------

### MAESTRO Admin User Management

Source: https://github.com/murtaza-nasir/maestro/blob/main/SETUP_GUIDE.md

Features for managing users within the MAESTRO system, accessible only to administrators.

```APIDOC
Admin User Management:
  View Users: 
    - List all registered users, their roles, and status.
  Create User: 
    - Create new user accounts.
  Manage User Accounts:
    Activate/Deactivate: 
      - Enable or disable user accounts.
    Promote/Revoke Admin: 
      - Grant or remove administrator privileges.
    Edit Details: 
      - Modify user information.
    Delete User: 
      - Remove user accounts.
```

--------------------------------

### MAESTRO CLI: Document Search

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Command to search documents for a specific user with a query and an optional limit.

```bash
docker compose --profile cli run --rm cli python cli_ingest.py search myusername "machine learning" --limit 5
```

--------------------------------

### Configure MAESTRO Environment

Source: https://github.com/murtaza-nasir/maestro/blob/main/README.md

Command to run the interactive setup script for configuring MAESTRO's environment variables.

```bash
./setup-env.sh
```

--------------------------------

### MAESTRO CLI Direct Docker Compose Execution

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Demonstrates how to execute MAESTRO CLI commands directly using Docker Compose. This is useful for running specific administrative tasks or when not using the helper script.

```bash
# General CLI command format
docker compose --profile cli run --rm cli python cli_ingest.py [command] [options]
```

--------------------------------

### MAESTRO CLI Document Ingestion

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Instructions for bulk ingesting PDF documents into MAESTRO using the CLI. Covers specifying the user, group, and directory, along with optional parameters like batch size and forcing re-embedding.

```python
docker compose --profile cli run --rm cli python cli_ingest.py ingest myusername GROUP_ID /app/pdfs
```

```python
docker compose --profile cli run --rm cli python cli_ingest.py ingest myusername abc123-def456 /app/pdfs --batch-size 10 --force-reembed
```

--------------------------------

### Verify GPU Access with Docker

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Executes `nvidia-smi` within the MAESTRO backend Docker container to verify that the container has access to the host system's NVIDIA GPU.

```bash
docker compose exec backend nvidia-smi
```

--------------------------------

### MAESTRO CLI Document Group Management

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Commands for managing document groups using the MAESTRO CLI. This includes creating new groups with descriptions and listing groups, either for a specific user or all groups.

```python
docker compose --profile cli run --rm cli python cli_ingest.py create-group myusername "Research Papers" --description "Collection of research papers"
```

```python
docker compose --profile cli run --rm cli python cli_ingest.py list-groups --user myusername
```

```python
docker compose --profile cli run --rm cli python cli_ingest.py list-groups
```

--------------------------------

### Create PDF Ingestion Directory

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Creates a directory named `pdfs` in the project root. This directory is used to store PDF files that will be ingested by the MAESTRO CLI for analysis.

```bash
mkdir -p pdfs
```

--------------------------------

### Configure GPU Device ID in Docker Compose

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Demonstrates how to specify which GPU device a Docker container should use by modifying the `device_ids` under the `deploy.resources.reservations.devices` section in `docker-compose.yml`.

```yaml
deploy:
  resources:
    reservations:
      devices:
        - driver: nvidia
          device_ids: ['0']  # Use GPU 0 instead
          capabilities: [gpu]
```

--------------------------------

### Build and Run MAESTRO with Docker Compose

Source: https://github.com/murtaza-nasir/maestro/blob/main/README.md

Command to build Docker images and start MAESTRO services in detached mode.

```bash
docker compose up --build -d
```

--------------------------------

### Docker Compose Persistent Caching

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Configuration for Docker Compose to mount persistent volumes for caching Hugging Face and Datalab models. This ensures models are available between container restarts, speeding up subsequent runs.

```yaml
volumes:
  - ./maestro_model_cache:/root/.cache/huggingface      # Embedding models
  - ./maestro_datalab_cache:/root/.cache/datalab       # Document processing models
```

--------------------------------

### React-Specific ESLint Configuration

Source: https://github.com/murtaza-nasir/maestro/blob/main/maestro_frontend/README.md

Enhances ESLint configuration by integrating rules from `eslint-plugin-react-x` and `eslint-plugin-react-dom` for comprehensive React and React DOM linting. It also includes project configuration for type checking.

```javascript
// eslint.config.js
import reactX from 'eslint-plugin-react-x'
import reactDom from 'eslint-plugin-react-dom'

export default tseslint.config([
  globalIgnores(['dist']),
  {
    files: ['**/*.{ts,tsx}'],
    extends: [
      // Other configs...
      // Enable lint rules for React
      reactX.configs['recommended-typescript'],
      // Enable lint rules for React DOM
      reactDom.configs.recommended,
    ],
    languageOptions: {
      parserOptions: {
        project: ['./tsconfig.node.json', './tsconfig.app.json'],
        tsconfigRootDir: import.meta.dirname,
      },
      // other options...
    },
  },
])
```

--------------------------------

### OpenRouter Models Configuration

Source: https://github.com/murtaza-nasir/maestro/blob/main/example reports/2025-05-03_22-44-24_ui_report_49bf6874-38ff-413e-b12e-9be566e1d627.md

Configuration details for OpenRouter models, specifying 'Light', 'Heavy', and 'Beast' presets, all mapped to 'openai/gpt-4o-mini'. This section also includes project statistics like total cost, token usage, and web searches.

```text
Project: /murtaza-nasir/maestro

OpenRouter Models Configured:
  Light: openai/gpt-4o-mini
  Heavy: openai/gpt-4o-mini
  Beast: openai/gpt-4o-mini

Stats:
  Total Cost: $2.051767
  Total Native Tokens: 7258662
  Total Web Searches: 159

Generated via: Streamlit UI
```

--------------------------------

### MAESTRO CLI: Check Processing Status

Source: https://github.com/murtaza-nasir/maestro/blob/main/DOCKER.md

Commands to check the processing status of documents for a specific user, a group, or all documents (admin).

```bash
docker compose --profile cli run --rm cli python cli_ingest.py status --user myusername
```

```bash
docker compose --profile cli run --rm cli python cli_ingest.py status --user myusername --group GROUP_ID
```

```bash
docker compose --profile cli run --rm cli python cli_ingest.py status
```

--------------------------------

### Contributing Guidelines

Source: https://github.com/murtaza-nasir/maestro/blob/main/README.md

The project welcomes feedback, bug reports, and feature suggestions. Users are encouraged to open an Issue on the GitHub repository to contribute.

```Markdown
# Contributing

Feedback, bug reports, and feature suggestions are highly valuable. Please feel free to open an Issue on the GitHub repository.
```

--------------------------------

### Backend Server Dependencies

Source: https://github.com/murtaza-nasir/maestro/blob/main/maestro_backend/requirements.txt

These packages are essential for setting up and running the backend server, including web framework, ASGI server, database ORM, and authentication.

```shell
fastapi
uvicorn[standard]
sqlalchemy
passlib[bcrypt]
python-jose[cryptography]
python-multipart
```

--------------------------------

### Clone MAESTRO Repository

Source: https://github.com/murtaza-nasir/maestro/blob/main/README.md

Command to clone the MAESTRO project repository from GitHub.

```bash
git clone https://github.com/murtaza-nasir/maestro.git
cd maestro
```

--------------------------------

### Custom Log Processing

Source: https://github.com/murtaza-nasir/maestro/blob/main/maestro_frontend/src/components/mission/README.md

Provides an example of transforming raw log data into a format suitable for the frontend. This includes ensuring proper timestamp formatting and allowing for additional custom processing.

```javascript
// Transform backend logs to frontend format
const processedLogs = rawLogs.map(log => ({
  ...log,
  timestamp: log.timestamp || new Date().toISOString(),
  // Add any additional processing
}));
```

--------------------------------

### MAESTRO Agent Role Recommendations

Source: https://github.com/murtaza-nasir/maestro/blob/main/VERIFIER_AND_MODEL_FINDINGS.md

Recommendations for LLM models suitable for different MAESTRO agent roles, categorized by API choices and self-hosted options.

```APIDOC
Planning Agent (FAST_LLM_PROVIDER):
  Top API Choice:
    - Model: openai/gpt-4o-mini
    - Rationale: Good balance of capability and cost for frequent, less computationally expensive tasks like planning.

  Self-Hosted Recommendation:
    - Model: qwen/qwen3-8b
    - Rationale: Good performance for its size, balancing speed and capability for planning.

  Alternative API Choice:
    - Model: qwen/qwen3-14b (via API)
    - Rationale: Reasonable cost profile and API performance for planning tasks.

Research Agent & Writing Agent (MID_LLM_PROVIDER):
  Top Tier API Choices:
    - Models: qwen/qwen3-14b, qwen/qwen3-30b-a3b, qwen/qwen-2.5-72b-instruct
    - Rationale: Very good aggregated performance in note generation and writing synthesis.

  Strong Alternative API Choices:
    - Models: google/gemini-2.5-flash-preview, google/gemma-3-27b-it
    - Rationale: Especially strong for accurate note generation.
    - Model: anthropic/claude-3.7-sonnet
    - Rationale: Consistently performed well for both note-taking and writing.

  Self-Hosted Recommendations:
    - Model: qwen/qwen3-14b
    - Rationale: Highest overall aggregated score for research/writing, suitable for challenging mid-tier tasks.
    - Model: google/gemma-3-27b-it
    - Rationale: Strong open model from Google, performed well, especially for note generation.
    - Model: qwen/qwen3-30b-a3b
    - Rationale: Strong performer in the Qwen family.

  Strategic Mix Example:
    - Research Agent: google/gemini-2.5-flash-preview (API) or google/gemma-3-27b-it (Self-Hosted) for strong note-taking.
    - Writing Agent: qwen/qwen3-14b (API or Self-Hosted) for strong writing scores.
```

--------------------------------

### Type-Aware ESLint Configuration

Source: https://github.com/murtaza-nasir/maestro/blob/main/maestro_frontend/README.md

Configures ESLint to enable type-aware lint rules for TypeScript files in a Vite project. It replaces default recommended rules with stricter type-checked alternatives and specifies project configuration files.

```javascript
export default tseslint.config([
  globalIgnores(['dist']),
  {
    files: ['**/*.{ts,tsx}'],
    extends: [
      // Other configs...

      // Remove tseslint.configs.recommended and replace with this
      ...tseslint.configs.recommendedTypeChecked,
      // Alternatively, use this for stricter rules
      ...tseslint.configs.strictTypeChecked,
      // Optionally, add this for stylistic rules
      ...tseslint.configs.stylisticTypeChecked,

      // Other configs...
    ],
    languageOptions: {
      parserOptions: {
        project: ['./tsconfig.node.json', './tsconfig.app.json'],
        tsconfigRootDir: import.meta.dirname,
      },
      // other options...
    },
  },
])
```

--------------------------------

### Facilitated Modelling in Stakeholder Engagement

Source: https://github.com/murtaza-nasir/maestro/blob/main/example reports/research_report_c3cf6ddb-4ea5-425a-82fa-de90ab10ca6d.md

Facilitated modelling involves practitioners guiding groups through structured problem-solving using tools like problem structuring methods and decision analysis. This is critical for incorporating stakeholder perspectives effectively.

```APIDOC
Facilitated Modelling:
  Description: Practitioners guide groups through structured problem-solving.
  Tools: Problem structuring methods, decision analysis.
  Objective: Ensure stakeholder perspectives are incorporated effectively.
  Context: Critical approach within stakeholder-based frameworks.
  Related Concepts: Cognitive mapping, strategic choice approach.
```

--------------------------------

### Utilities and Web Scraping Libraries

Source: https://github.com/murtaza-nasir/maestro/blob/main/maestro_backend/ai_researcher/requirements.txt

General utility libraries, including progress bars, markdown processing, asynchronous file I/O, and web content extraction.

```python
tqdm
markdown
aiofiles
tavily-python
newspaper3k
lxml[html_clean]
lxml_html_clean
linkup-sdk
```

--------------------------------

### Machine Learning and Tabular Data

Source: https://github.com/murtaza-nasir/maestro/blob/main/example reports/2025-05-03_22-44-24_ui_report_49bf6874-38ff-413e-b12e-9be566e1d627.md

This Reddit discussion relates to a novel model for tabular data, IGANN, within the Machine Learning community.

```Research Reference
The heart of the internet.
Available at: https://www.reddit.com/r/MachineLearning/comments/155rav2/n_novel_model_for_tabular_data_igann_looks_like_a/
```

--------------------------------

### FastAPI Backend

Source: https://github.com/murtaza-nasir/maestro/blob/main/README.md

The backend API is built with FastAPI, handling authentication, mission control, agentic logic, and the RAG pipeline. It utilizes SQLAlchemy and SQLite for database management.

```Python
from fastapi import FastAPI
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker

app = FastAPI()

# Database setup
SQLALCHEMY_DATABASE_URL = "sqlite:///./maestro.db"
engine = create_engine(SQLALCHEMY_DATABASE_URL)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

# Example route
@app.get("/")
def read_root():
    return {"Hello": "MAESTRO"}

# Add more routes for authentication, mission control, agentic logic, RAG pipeline...
```

--------------------------------

### Benchmarking Generative AI Performance

Source: https://github.com/murtaza-nasir/maestro/blob/main/example reports/2025-05-03_22-44-24_ui_report_49bf6874-38ff-413e-b12e-9be566e1d627.md

This resource discusses the approach required for benchmarking the performance of Generative AI, emphasizing a holistic methodology.

```Research Reference
Benchmarking Generative AI Performance Requires a Holistic Approach.
Available at: https://link.springer.com/chapter/10.1007/978-3-031-68031-1_3
```

--------------------------------

### CLI for Administrative Tasks

Source: https://github.com/murtaza-nasir/maestro/blob/main/README.md

A Command Line Interface (CLI) is available for administrative tasks such as bulk document ingestion and user management. Specific commands would be defined within the CLI implementation.

```Shell
# Example CLI usage (conceptual)
# python maestro_cli.py ingest --path /path/to/documents
# python maestro_cli.py users create --username admin --password secret
# python maestro_cli.py status
```

--------------------------------

### UI Libraries

Source: https://github.com/murtaza-nasir/maestro/blob/main/maestro_backend/ai_researcher/requirements.txt

Libraries for creating user interfaces for the project.

```python
streamlit
gradio
```

--------------------------------

### OpenRouter Models Configuration

Source: https://github.com/murtaza-nasir/maestro/blob/main/example reports/2025-05-09_04-27-23_ui_report_4615084c-6fe6-4587-8f3f-3587954ce3a0.md

Configuration details for OpenRouter models, including assigned aliases for different usage tiers (Light, Heavy, Beast) and associated costs and token usage statistics.

```APIDOC
OpenRouter Models Configured:
  Light: openai/gpt-4o-mini
  Heavy: google/gemma-3-27b-it
  Beast: google/gemma-3-27b-it

Stats:
  Total Cost: $1.134722
  Total Native Tokens: 3181402
  Total Web Searches: 159
Generated via: Streamlit UI
```

--------------------------------

### LLM Interaction Libraries

Source: https://github.com/murtaza-nasir/maestro/blob/main/maestro_backend/ai_researcher/requirements.txt

Libraries for interacting with Large Language Models, including API clients and environment variable management.

```python
openai
python-dotenv
requests
httpx
```

--------------------------------

### Generative AI in Teaching and Learning

Source: https://github.com/murtaza-nasir/maestro/blob/main/example reports/2025-05-03_22-44-24_ui_report_49bf6874-38ff-413e-b12e-9be566e1d627.md

This resource focuses on the application of Generative AI tools within educational settings, specifically for teaching and learning purposes.

```Research Reference
Generative AI Tools for Teaching and Learning | Center for Teaching & Learning | Utah Tech University.
Available at: https://ctl.utahtech.edu/aitools/
```

```Research Reference
AI & Accessibility.
Available at: https://teaching.cornell.edu/generative-artificial-intelligence/ai-accessibility
```