From 584ea3ecb901586cc77c7386d98797e1db2380e0 Mon Sep 17 00:00:00 2001 From: Vineeth Kalluru Date: Fri, 6 Jun 2025 11:57:07 -0500 Subject: [PATCH 01/11] Test commit Signed-off-by: Vineeth Kalluru --- industries/manufacturing/README.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 industries/manufacturing/README.md diff --git a/industries/manufacturing/README.md b/industries/manufacturing/README.md new file mode 100644 index 000000000..e69de29bb From b1cf8c2ea2586b72d325bb478a3a1fada2f9bdb6 Mon Sep 17 00:00:00 2001 From: Vineeth Kalluru Date: Fri, 6 Jun 2025 14:15:26 -0500 Subject: [PATCH 02/11] Git ignore with new README Signed-off-by: Vineeth Kalluru --- .gitignore | 3 +++ industries/manufacturing/README.md | 0 2 files changed, 3 insertions(+) delete mode 100644 industries/manufacturing/README.md diff --git a/.gitignore b/.gitignore index 4d9702cc6..deaecfc32 100644 --- a/.gitignore +++ b/.gitignore @@ -32,3 +32,6 @@ RAG/notebooks/langchain/data/save_embedding # 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Traditional reactive maintenance approaches wait for equipment to fail before taking action, while scheduled preventive maintenance often leads to unnecessary interventions and costs. + +**Predictive maintenance** revolutionizes this approach by: + +- **Preventing Costly Downtime**: Identifying potential failures before they occur, allowing for planned maintenance windows +- **Optimizing Maintenance Schedules**: Performing maintenance only when needed, reducing unnecessary interventions +- **Extending Equipment Lifespan**: Monitoring equipment health to maximize operational efficiency +- **Improving Safety**: Preventing catastrophic failures that could endanger personnel +- **Reducing Costs**: Minimizing emergency repairs, spare parts inventory, and operational disruptions + +This predictive maintenance agent leverages AI and machine learning to analyze sensor data from turbofan engines, predict remaining useful life (RUL), and provide actionable insights for maintenance teams. + +## NASA Turbofan Engine Dataset + +The system utilizes the **NASA Turbofan Engine Degradation Simulation Dataset (C-MAPSS)**, a widely-used benchmark dataset for prognostics and health management research in manufacturing domain. + +### Dataset Overview + +The NASA dataset contains simulated turbofan engine run-to-failure data with the following characteristics: + +- **21 Sensor Measurements**: Including temperature, pressure, vibration, and flow measurements +- **3 Operational Settings**: Different flight conditions and altitudes +- **Multiple Engine Units**: Each with unique degradation patterns +- **Run-to-Failure Data**: Complete lifecycle from healthy operation to failure + +### Key Sensor Parameters + +| Sensor ID | Description | Unit | +|-----------|-------------|------| +| sensor_1 | Total temperature at fan inlet | °R | +| sensor_2 | Total temperature at LPC outlet | °R | +| sensor_3 | Total temperature at HPC outlet | °R | +| sensor_4 | Total temperature at LPT outlet | °R | +| sensor_11 | Static pressure at HPC outlet | psia | +| sensor_13 | Corrected fan speed | rpm | +| sensor_20 | Ratio of fuel flow to Ps30 | pps/psi | +| sensor_21 | Corrected fan speed | rpm | + +### Dataset Structure + +``` +Training Data: Engine run-to-failure trajectories +Test Data: Engine trajectories of unknown remaining cycles +Ground Truth: Actual RUL values for test engines +``` + +The dataset enables the development of prognostic models that can predict when an engine will require maintenance based on its current sensor readings and historical degradation patterns. + +## Architecture + +The Predictive Maintenance Agent follows a modular, multi-agent architecture built on the NVIDIA AIQ Toolkit framework: + +``` + +``` + +### Key Components + +#### 1. **React Agent Workflow** +- Main orchestration layer using ReAct (Reasoning + Acting) pattern +- Coordinates between different tools and agents +- Handles user queries and tool selection + +#### 2. **SQL Retriever Tool** +- Generates SQL queries using NIM LLM +- Retrieves sensor data from SQLite database +- Uses vector embeddings for semantic query understanding + +#### 3. **RUL Prediction Tool** +- Predicts Remaining Useful Life using pre-trained XGBoost model +- Processes sensor data through feature engineering pipeline +- Provides uncertainty estimates and confidence intervals + +#### 4. **Plotting Agent** +- Multi-tool agent for data visualization +- Generates interactive charts and plots +- Supports line charts, distributions, and comparisons + +#### 5. **Vector Database** +- ChromaDB for storing and retrieving database schema information +- Enables semantic search over table structures and relationships + +## Setup and Installation + +### Prerequisites + +- Python 3.11+ (< 3.13) +- Conda or Miniconda +- NVIDIA NIM API access +- Node.js v18+ (for AgentIQ web interface) +- Docker (for Phoenix observability - optional) + +### 1. Create a New Conda Environment + +Create and activate a dedicated conda environment for the predictive maintenance agent: + +```bash +# Create new conda environment +conda create -n pdm python=3.11 + +# Activate the environment +conda activate pdm +``` + +### 2. Install NVIDIA AgentIQ from Source + +Clone and install the NVIDIA AIQToolkit following the [official installation guide](https://docs.nvidia.com/agentiq/latest/intro/install.html#install-from-source): + +```bash +# Clone the AIQToolkit repository +git clone https://github.com/NVIDIA/AIQToolkit.git +cd AIQToolkit + +# Install AIQ from source +uv pip install -e . + +# Verify installation +aiq --help + +# Optional: Remove the cloned repository after installation +# You can safely delete the AIQToolkit directory once installation is complete +# cd .. && rm -rf AIQToolkit +``` + +### 3. Install the Predictive Maintenance Workflow + +Now install the predictive maintenance agent as a custom workflow + +```bash +# Navigate back to your working directory +cd .. + +# Clone the Generative AI examples repository +git clone https://github.com/NVIDIA/GenerativeAIExamples.git +cd GenerativeAIExamples/industries/manufacturing/predictive_maintenance_agent + +# Install the workflow and its dependencies +uv pip install -e . +``` + +This will install the predictive maintenance agent as a custom AgentIQ workflow with all required dependencies. + +### 4. Environment Setup + +Create a `.env` file in the predictive maintenance agent directory with your NVIDIA NIM credentials: + +```bash +# .env file +NVIDIA_API_KEY=your_nvidia_api_key_here +NIM_BASE_URL=https://integrate.api.nvidia.com/v1 +``` + +**Important**: Make sure to replace `your_nvidia_api_key_here` with your actual NVIDIA API key. + +The environment variables need to be loaded before starting the AIQ server. You have several options: + +**Source the environment manually** +```bash +# Load environment variables manually (Linux/Mac) +export $(cat .env | grep -v '^#' | xargs) + +# For Windows Command Prompt +for /f "tokens=*" %i in (.env) do set %i + +# For Windows PowerShell +Get-Content .env | ForEach-Object { if ($_ -match "^([^#].*)=(.*)") { [Environment]::SetEnvironmentVariable($matches[1], $matches[2]) } } +``` + +### 5. Database Setup + +The predictive maintenance agent requires the NASA turbofan dataset to be loaded into a SQLite database. We provide a comprehensive setup script to convert the original NASA text files into a structured database. + +#### Download the NASA Dataset + +First, download the NASA Turbofan Engine Degradation Simulation Dataset (C-MAPSS): + +1. Visit the [NASA Prognostics Data Repository](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/) +2. Download the "Turbofan Engine Degradation Simulation Data Set" +3. Extract all text files to a `data/` directory in your project + +The dataset should include these files: +``` +data/ +├── train_FD001.txt # Training data - Sea level, single fault mode +├── train_FD002.txt # Training data - Sea level, multiple fault modes +├── train_FD003.txt # Training data - High altitude, single fault mode +├── train_FD004.txt # Training data - High altitude, multiple fault modes +├── test_FD001.txt # Test data - Sea level, single fault mode +├── test_FD002.txt # Test data - Sea level, multiple fault modes +├── test_FD003.txt # Test data - High altitude, single fault mode +├── test_FD004.txt # Test data - High altitude, multiple fault modes +├── RUL_FD001.txt # Ground truth RUL values for test data +├── RUL_FD002.txt # Ground truth RUL values for test data +├── RUL_FD003.txt # Ground truth RUL values for test data +└── RUL_FD004.txt # Ground truth RUL values for test data +``` + +#### Run the Database Setup Script + +Convert the NASA text files to SQLite database: + +```bash +# Basic usage (assumes data/ directory exists) +python setup_database.py + +# Custom data directory and database path +python setup_database.py --data-dir /path/to/nasa/files --db-path /path/to/output.db + +# Get help on available options +python setup_database.py --help +``` + +The script will create a comprehensive SQLite database with the following structure: + +#### Database Tables Created + +| Table Name | Description | Records | +|------------|-------------|---------| +| `training_data` | Complete engine run-to-failure trajectories | ~20,000+ | +| `test_data` | Engine test trajectories (unknown RUL) | ~13,000+ | +| `rul_data` | Ground truth RUL values for test engines | ~400+ | +| `sensor_metadata` | Descriptions of all 21 sensors | 21 | +| `dataset_metadata` | Information about the 4 sub-datasets | 4 | + +#### Database Views Created + +| View Name | Purpose | +|-----------|---------| +| `latest_sensor_readings` | Most recent sensor readings per engine | +| `engine_health_summary` | Aggregated health statistics per engine | + +#### Sample Database Queries + +Once the database is set up, you can run sample queries: + +```sql +-- View engine health summary +SELECT * FROM engine_health_summary LIMIT 5; + +-- Get sensor readings for a specific engine +SELECT unit_number, time_in_cycles, sensor_measurement_1, sensor_measurement_11, RUL +FROM training_data +WHERE unit_number = 1 AND dataset = 'FD001' +ORDER BY time_in_cycles; + +-- Find engines with shortest operational life +SELECT unit_number, dataset, MAX(time_in_cycles) as total_cycles +FROM training_data +GROUP BY unit_number, dataset +ORDER BY total_cycles +LIMIT 10; +``` + +#### Verification + +The setup script automatically validates the database creation. You should see output similar to: + +``` +✅ Database created successfully at: PredM_db/nasa_turbo.db + +Database contains the following tables: +- training_data: Engine run-to-failure trajectories +- test_data: Engine test trajectories +- rul_data: Ground truth RUL values +- sensor_metadata: Sensor descriptions +- dataset_metadata: Dataset information + +Useful views created: +- latest_sensor_readings: Latest readings per engine +- engine_health_summary: Engine health statistics +``` + +### 6. Configure Paths + +Update the paths in `configs/config.yml` to match your local environment: + +```yaml +functions: + sql_retriever: + vector_store_path: "/path/to/your/PredM_db" + db_path: "/path/to/your/PredM_db/nasa_turbo.db" + output_folder: "/path/to/your/output_data" + predict_rul: + scaler_path: "/path/to/your/models/scaler_model.pkl" + model_path: "/path/to/your/models/xgb_model_fd001.pkl" + output_folder: "/path/to/your/output_data" +``` + +### 7. Verify Installation + +Verify that both AIQ and the workflow are properly installed: + +```bash +pip list | grep -E "aiq|predictive" +``` + +## Launching AIQ Server and Frontend + +### 1. Start the AIQ Server + +**Important**: Ensure environment variables are loaded before starting the server. + +#### Load environment variables + +If you prefer to load environment variables manually: + +```bash +# Linux/Mac: Load environment variables and start server +export $(cat .env | grep -v '^#' | xargs) && aiq serve --config_file=configs/config.yml + +# Alternative: Source variables first, then start server +source <(cat .env | grep -v '^#' | sed 's/^/export /') +aiq serve --config_file=configs/config.yml +``` + +The server will start on `http://localhost:8000` by default and you should see output similar to: + +``` +2025-03-07 12:54:20,394 - aiq.cli.commands.start - INFO - Starting AgentIQ from config file: 'configs/config.yml' +INFO: Started server process [47250] +INFO: Waiting for application startup. +INFO: Application startup complete. +INFO: Uvicorn running on http://localhost:8000 (Press CTRL+C to quit) +``` + +**Note**: If you see any errors related to missing API keys, verify that your `.env` file is properly formatted and that environment variables are loaded correctly. + +### 2. Launch the AgentIQ Web User Interface + +AgentIQ provides a dedicated web interface that requires Node.js v18+. + +#### Setup the AIQtoolkit UI + +The NVIDIA AIQToolkit UI provides a modern web interface for interacting with AIQ workflows. Follow these steps to set up the UI: + +**Prerequisites:** +- Node.js v18+ +- Git + +**Installation:** + +1. **Clone the AIQToolkit-UI repository:** + ```bash + git clone https://github.com/NVIDIA/AIQToolkit-UI.git + cd AIQToolkit-UI + ``` + +2. **Install dependencies:** + ```bash + npm ci + ``` + +3. **Run the development server:** + ```bash + npm run dev + ``` + + The application will be available at `http://localhost:3000` + +**Alternative: Docker Deployment** + +For production or containerized environments: + +```bash +# Build the Docker image +docker build -t aiqtoolkit-ui . + +# Run the container +docker run -p 3000:3000 aiqtoolkit-ui +``` + +#### Configure the UI Settings + +Once the UI is running: + +1. Open the web interface in your browser at `http://localhost:3000` +2. Click the **Settings** icon in the bottom left corner +3. Configure the connection settings: + - **HTTP URL for Chat Completion**: Choose your preferred endpoint: + - `/generate` - Non-streaming responses + - `/generate/stream` - Streaming responses (recommended) + - `/chat` - Chat-based non-streaming + - `/chat/stream` - Chat-based streaming (recommended for intermediate results) + - **Theme**: Select Light or Dark theme + - **WebSocket URL**: For real-time connections (if needed) + +**Note**: The UI supports both HTTP requests (OpenAI compatible) and WebSocket connections for server communication. + +### 3. Alternative: Direct API Interaction + +You can also interact with the server directly via REST API: + +```bash +# Non-streaming request +curl -X POST http://localhost:8000/generate \ + -H "Content-Type: application/json" \ + -d '{"input_message": "What is the current health status of engine unit 1?", "use_knowledge_base": true}' + +# Chat-based request +curl -X POST http://localhost:8000/chat \ + -H "Content-Type: application/json" \ + -d '{"message": "What is the current health status of engine unit 1?"}' +``` + +### 4. Verify Installation + +Once both server and frontend are running: + +1. Navigate to the web interface (`http://localhost:3000`) +2. Configure the settings to point to your server +3. Test with a simple question like: "Show me sensor data for engine unit 1" +4. Verify you receive both intermediate steps and final results + +## Testing with Example Prompts + +Here are comprehensive example prompts to test different capabilities of the predictive maintenance agent: + +### Data Retrieval and Analysis + +* **Basic Data Query** + ``` + Show me the sensor readings for engine unit 1 for the last 50 cycles. + ``` + +* **Sensor Analysis** + ``` + What are the temperature sensor readings (sensors 1-4) for engine units 1-5 over their operational lifetime? + ``` + +* **Operational Settings Analysis** + ``` + Compare the operational settings across different engine units and show me which units operated under the most severe conditions. + ``` + +* **Operational Settings Data Retrieval** + ``` + Retrieve the time_in_cycles and operational_setting_1 from the test_FD001 table for unit number 1. Then plot operational_setting_1 over time_in_cycles. + ``` + +* **RUL Data Analysis** + ``` + Retrieve RUL of each unit from the train_FD001 table. Then plot the distribution of RUL. + ``` + +### Data Visualization + +* **Trend Analysis** + ``` + Plot the degradation trend of sensor_measurement_11 vs time_in_cycles for engine unit 2. + ``` + +* **Multi-Engine Comparison** + ``` + Create a comparison plot showing sensor_measurement_21 for engines 1 and 2 to identify different degradation patterns. + ``` + +* **Distribution Analysis** + ``` + Show me the distribution of sensor_measurement_4 values across all engine units to identify outliers. + ``` + +### Predictive Analytics + +* **RUL Prediction** + ``` + Predict the remaining useful life for engine unit 5 based on its current sensor readings. + ``` + +### Advanced Queries + +* **Comprehensive RUL Analysis with Comparison** + ``` + Retrieve time in cycles, all sensor measurements and RUL value for engine unit 24 from FD001 test and RUL tables. Predict RUL for it. Finally, generate a plot to compare actual RUL value with predicted RUL value across time. + ``` + +* **Anomaly Detection** + ``` + Identify any anomalous sensor readings in the last 100 cycles for all active engines. + ``` + +## Appendix: Phoenix Observability + +[Phoenix](https://phoenix.arize.com/) provides comprehensive observability and monitoring for your AI applications, enabling you to track performance, debug issues, and optimize your predictive maintenance system. + +### Starting Phoenix + +#### Option 1: Docker (Recommended) + +```bash +# Start Phoenix server +docker run -p 6006:6006 -p 4317:4317 arizephoenix/phoenix:latest + +# Phoenix will be available at http://localhost:6006 +``` + +#### Option 2: Python Package + +```bash +# Install Phoenix +uv pip install arize-phoenix + +# Start Phoenix server +phoenix serve +``` + +### Configuration + +Phoenix is pre-configured in `configs/config.yml`: + +```yaml +general: + tracing: + phoenix: + _type: phoenix + endpoint: http://localhost:6006/v1/traces + project: predictive-maintenance-app +``` + +### Monitoring Capabilities + +Once Phoenix is running and your agents are active, you can monitor: + +1. **Trace Analysis**: View detailed execution traces of agent workflows +2. **LLM Interactions**: Monitor all LLM calls, prompts, and responses +3. **Tool Usage**: Track which tools are being used and their performance +4. **Error Detection**: Identify and debug failures in the agent pipeline +5. **Performance Metrics**: Analyze response times and system performance +6. **Cost Tracking**: Monitor API usage and associated costs + +### Accessing Phoenix Dashboard + +1. Navigate to `http://localhost:6006` +2. Select the `predictive-maintenance-app` project +3. Explore traces, sessions, and performance metrics +4. Use filters to focus on specific time periods or components + +### Best Practices + +- **Regular Monitoring**: Check Phoenix dashboard regularly for performance insights +- **Error Analysis**: Use trace data to quickly identify and resolve issues +- **Optimization**: Identify slow components and optimize based on performance data +- **Cost Management**: Monitor API usage to optimize costs + +## Next Steps + +The predictive maintenance agent provides a solid foundation for industrial AI applications. Here are planned enhancements to further improve its capabilities: + +### 1. Adding Memory Layer + +**Objective**: Enable the agent to remember previous interactions and build context over time. + +**Implementation Plan**: +- Integrate conversation memory using LangChain's memory components +- Store user preferences and historical analysis patterns +- Implement semantic memory for retaining domain knowledge +- Add session persistence for multi-turn conversations + +**Benefits**: +- Improved user experience with contextual responses +- Better understanding of recurring maintenance patterns +- Personalized recommendations based on user history + +### 2. Parallel Tool Calls for Data Visualization + +**Objective**: Enhance performance by executing multiple visualization tasks simultaneously. + +**Implementation Plan**: +- Implement async tool execution framework +- Create parallel plotting workflows for multiple engine units +- Add batch processing for large-scale data analysis +- Optimize resource utilization for concurrent operations + +**Benefits**: +- Significantly faster response times for complex queries +- Improved system scalability +- Enhanced user experience with rapid visualizations + +### 3. Action Recommendation Reasoning Agent + +**Objective**: Develop an intelligent agent that provides specific maintenance recommendations. + +**Implementation Plan**: +- Create a specialized reasoning agent using advanced prompt engineering +- Integrate maintenance best practices and industry standards +- Implement cost-benefit analysis for maintenance decisions +- Add integration with maintenance management systems + +**Capabilities**: +- Prioritized maintenance recommendations +- Risk assessment and mitigation strategies +- Resource allocation optimization +- Compliance with safety regulations + +### 4. Fault Detection Agent + +**Objective**: Implement real-time fault detection and classification capabilities. + +**Implementation Plan**: +- Develop anomaly detection algorithms using statistical methods +- Implement pattern recognition for fault signatures +- Create alert systems for critical failures +- Add fault classification and root cause analysis + +**Features**: +- Real-time monitoring and alerting +- Automated fault classification +- Historical fault pattern analysis +- Integration with existing monitoring systems + +### 5. NV-Tesseract Foundation Model Integration + +**Objective**: Replace existing ML models with NVIDIA's state-of-the-art [NV-Tesseract time series foundation models](https://developer.nvidia.com/blog/new-nvidia-nv-tesseract-time-series-models-advance-dataset-processing-and-anomaly-detection/). + +#### About NV-Tesseract + +NV-Tesseract represents a breakthrough in time-series AI, offering specialized models for different predictive tasks: + +- **Advanced Architecture**: Transformer-based embeddings with multi-head attention layers +- **Modular Design**: Purpose-built models for anomaly detection, forecasting, and classification +- **Superior Performance**: 5-20% accuracy improvements over traditional methods +- **Industry Applications**: Proven success in manufacturing, finance, healthcare, and industrial processes + +#### Integration Plan + +**Phase 1: Model Evaluation** +- Benchmark NV-Tesseract against current XGBoost models +- Evaluate performance on NASA turbofan dataset +- Compare accuracy, precision, and recall metrics + +**Phase 2: Architecture Migration** +- Replace existing prediction pipeline with NV-Tesseract models +- Implement transformer-based feature processing +- Optimize inference performance for real-time predictions + +**Phase 3: Enhanced Capabilities** +- **Anomaly Detection**: Implement real-time operational anomaly detection with F1 scores up to 0.96 +- **Advanced Forecasting**: Add multi-horizon forecasting for maintenance planning +- **Classification**: Enhance fault type classification with improved accuracy + +**Expected Benefits**: +- **Improved Accuracy**: Superior prediction performance, especially on complex multivariate datasets +- **Better Generalization**: Enhanced ability to handle unfamiliar data patterns +- **Real-time Processing**: Optimized inference for production environments +- **Reduced False Positives**: More precise anomaly detection reducing unnecessary maintenance + +#### Implementation Timeline + +1. **Q2 2025**: NV-Tesseract model evaluation and integration planning +2. **Q3 2025**: Core model replacement and testing +3. **Q4 2025**: Production deployment and performance optimization + +### Getting Started with Next Steps + +To contribute to these enhancements: + +1. **Fork the Repository**: Create your own branch for development +2. **Review Current Architecture**: Understand the existing codebase structure +3. **Choose Enhancement**: Select a specific improvement area to focus on +4. **Development Plan**: Create detailed implementation plan +5. **Testing Strategy**: Develop comprehensive testing approach +6. **Documentation**: Update documentation and examples + +For more information about contributing or to discuss specific enhancements, please reach out to the development team. + +--- + +**Contact Information**: +- Author: Vineeth Kalluru +- Maintainer: NVIDIA Corporation +- Framework: NVIDIA AIQ Toolkit + +**Additional Resources**: +- [NVIDIA AIQ Toolkit Documentation](https://docs.nvidia.com/aiq-toolkit/) +- [NV-Tesseract Model Information](https://developer.nvidia.com/blog/new-nvidia-nv-tesseract-time-series-models-advance-dataset-processing-and-anomaly-detection/) +- [Phoenix Observability Platform](https://phoenix.arize.com/) \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/configs/config.yml b/industries/manufacturing/predictive_maintenance_agent/configs/config.yml new file mode 120000 index 000000000..113a15bec --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/configs/config.yml @@ -0,0 +1 @@ +/Users/vikalluru/Documents/GenerativeAIExamples/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/models/scaler_model.pkl b/industries/manufacturing/predictive_maintenance_agent/models/scaler_model.pkl new file mode 100644 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"0.1.0" +dependencies = [ + "aiqtoolkit[profiling, langchain, telemetry]", + "pydantic ~= 2.10.0, <2.11.0", +] +requires-python = ">=3.11,<3.13" +description = "Predictive maintenance workflow using AIQ" +classifiers = ["Programming Language :: Python"] +authors = [{ name = "Vineeth Kalluru" }] +maintainers = [{ name = "NVIDIA Corporation" }] + +[project.entry-points.'aiq.components'] +predictive_maintenance_agent = "predictive_maintenance_agent.register" \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/setup_database.py b/industries/manufacturing/predictive_maintenance_agent/setup_database.py new file mode 100644 index 000000000..4bf5ad222 --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/setup_database.py @@ -0,0 +1,359 @@ +#!/usr/bin/env python3 +""" +NASA Turbofan Engine Dataset to SQLite Database Converter + +This script converts the NASA Turbofan Engine Degradation Simulation Dataset (C-MAPSS) +from text files into a structured SQLite database for use with the predictive maintenance agent. + +The NASA dataset contains: +- Training data: Engine run-to-failure trajectories +- Test data: Engine trajectories of unknown remaining cycles +- RUL data: Ground truth remaining useful life values + +Dataset structure: +- unit_number: Engine unit identifier +- time_in_cycles: Operational time cycles +- operational_setting_1, 2, 3: Operating conditions +- sensor_measurement_1 to 21: Sensor readings +""" + +import sqlite3 +import pandas as pd +import numpy as np +import os +from pathlib import Path +import logging + +# Set up logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +class NASADatasetProcessor: + """Processes NASA Turbofan Engine Dataset and creates SQLite database.""" + + def __init__(self, data_dir: str = "data", db_path: str = "PredM_db/nasa_turbo.db"): + """ + Initialize the processor. + + Args: + data_dir: Directory containing NASA dataset text files + db_path: Path where SQLite database will be created + """ + self.data_dir = Path(data_dir) + self.db_path = Path(db_path) + + # Ensure database directory exists + self.db_path.parent.mkdir(exist_ok=True) + + # Define column names for the dataset + self.columns = [ + 'unit_number', 'time_in_cycles', + 'operational_setting_1', 'operational_setting_2', 'operational_setting_3', + 'sensor_measurement_1', 'sensor_measurement_2', 'sensor_measurement_3', + 'sensor_measurement_4', 'sensor_measurement_5', 'sensor_measurement_6', + 'sensor_measurement_7', 'sensor_measurement_8', 'sensor_measurement_9', + 'sensor_measurement_10', 'sensor_measurement_11', 'sensor_measurement_12', + 'sensor_measurement_13', 'sensor_measurement_14', 'sensor_measurement_15', + 'sensor_measurement_16', 'sensor_measurement_17', 'sensor_measurement_18', + 'sensor_measurement_19', 'sensor_measurement_20', 'sensor_measurement_21' + ] + + # Sensor descriptions for metadata + self.sensor_descriptions = { + 'sensor_measurement_1': 'Total temperature at fan inlet (°R)', + 'sensor_measurement_2': 'Total temperature at LPC outlet (°R)', + 'sensor_measurement_3': 'Total temperature at HPC outlet (°R)', + 'sensor_measurement_4': 'Total temperature at LPT outlet (°R)', + 'sensor_measurement_5': 'Pressure at fan inlet (psia)', + 'sensor_measurement_6': 'Total pressure in bypass-duct (psia)', + 'sensor_measurement_7': 'Total pressure at HPC outlet (psia)', + 'sensor_measurement_8': 'Physical fan speed (rpm)', + 'sensor_measurement_9': 'Physical core speed (rpm)', + 'sensor_measurement_10': 'Engine pressure ratio (P50/P2)', + 'sensor_measurement_11': 'Static pressure at HPC outlet (psia)', + 'sensor_measurement_12': 'Ratio of fuel flow to Ps30 (pps/psi)', + 'sensor_measurement_13': 'Corrected fan speed (rpm)', + 'sensor_measurement_14': 'Corrected core speed (rpm)', + 'sensor_measurement_15': 'Bypass Ratio', + 'sensor_measurement_16': 'Burner fuel-air ratio', + 'sensor_measurement_17': 'Bleed Enthalpy', + 'sensor_measurement_18': 'Required fan speed', + 'sensor_measurement_19': 'Required fan conversion speed', + 'sensor_measurement_20': 'High-pressure turbines Cool air flow', + 'sensor_measurement_21': 'Low-pressure turbines Cool air flow' + } + + def read_data_file(self, file_path: Path) -> pd.DataFrame: + """ + Read a NASA dataset text file and return as DataFrame. + + Args: + file_path: Path to the text file + + Returns: + DataFrame with proper column names + """ + try: + # Read space-separated text file + df = pd.read_csv(file_path, sep='\s+', header=None, names=self.columns) + logger.info(f"Loaded {len(df)} records from {file_path.name}") + return df + except Exception as e: + logger.error(f"Error reading {file_path}: {e}") + return pd.DataFrame() + + def process_training_data(self, conn: sqlite3.Connection): + """Process training data files and create database tables.""" + logger.info("Processing training data...") + + training_files = [ + 'train_FD001.txt', 'train_FD002.txt', 'train_FD003.txt', 'train_FD004.txt' + ] + + all_training_data = [] + + for file_name in training_files: + file_path = self.data_dir / file_name + if file_path.exists(): + df = self.read_data_file(file_path) + if not df.empty: + # Add dataset identifier + df['dataset'] = file_name.replace('train_', '').replace('.txt', '') + + # Calculate RUL for training data (max cycle - current cycle) + df['RUL'] = df.groupby('unit_number')['time_in_cycles'].transform('max') - df['time_in_cycles'] + + all_training_data.append(df) + else: + logger.warning(f"Training file not found: {file_path}") + + if all_training_data: + training_df = pd.concat(all_training_data, ignore_index=True) + training_df.to_sql('training_data', conn, if_exists='replace', index=False) + logger.info(f"Created training_data table with {len(training_df)} records") + + def process_test_data(self, conn: sqlite3.Connection): + """Process test data files and create database tables.""" + logger.info("Processing test data...") + + test_files = [ + 'test_FD001.txt', 'test_FD002.txt', 'test_FD003.txt', 'test_FD004.txt' + ] + + all_test_data = [] + + for file_name in test_files: + file_path = self.data_dir / file_name + if file_path.exists(): + df = self.read_data_file(file_path) + if not df.empty: + # Add dataset identifier + df['dataset'] = file_name.replace('test_', '').replace('.txt', '') + all_test_data.append(df) + else: + logger.warning(f"Test file not found: {file_path}") + + if all_test_data: + test_df = pd.concat(all_test_data, ignore_index=True) + test_df.to_sql('test_data', conn, if_exists='replace', index=False) + logger.info(f"Created test_data table with {len(test_df)} records") + + def process_rul_data(self, conn: sqlite3.Connection): + """Process RUL (Remaining Useful Life) data files.""" + logger.info("Processing RUL data...") + + rul_files = [ + 'RUL_FD001.txt', 'RUL_FD002.txt', 'RUL_FD003.txt', 'RUL_FD004.txt' + ] + + all_rul_data = [] + + for file_name in rul_files: + file_path = self.data_dir / file_name + if file_path.exists(): + try: + # RUL files contain one RUL value per line for each test engine + rul_values = pd.read_csv(file_path, header=None, names=['RUL']) + rul_values['unit_number'] = range(1, len(rul_values) + 1) + rul_values['dataset'] = file_name.replace('RUL_', '').replace('.txt', '') + all_rul_data.append(rul_values[['unit_number', 'dataset', 'RUL']]) + logger.info(f"Loaded {len(rul_values)} RUL values from {file_name}") + except Exception as e: + logger.error(f"Error reading RUL file {file_path}: {e}") + else: + logger.warning(f"RUL file not found: {file_path}") + + if all_rul_data: + rul_df = pd.concat(all_rul_data, ignore_index=True) + rul_df.to_sql('rul_data', conn, if_exists='replace', index=False) + logger.info(f"Created rul_data table with {len(rul_df)} records") + + def create_metadata_tables(self, conn: sqlite3.Connection): + """Create metadata tables with sensor descriptions and dataset information.""" + logger.info("Creating metadata tables...") + + # Sensor metadata + sensor_metadata = pd.DataFrame([ + {'sensor_name': sensor, 'description': desc} + for sensor, desc in self.sensor_descriptions.items() + ]) + sensor_metadata.to_sql('sensor_metadata', conn, if_exists='replace', index=False) + + # Dataset metadata + dataset_metadata = pd.DataFrame([ + {'dataset': 'FD001', 'description': 'Sea level conditions', 'fault_modes': 1}, + {'dataset': 'FD002', 'description': 'Sea level conditions', 'fault_modes': 6}, + {'dataset': 'FD003', 'description': 'High altitude conditions', 'fault_modes': 1}, + {'dataset': 'FD004', 'description': 'High altitude conditions', 'fault_modes': 6} + ]) + dataset_metadata.to_sql('dataset_metadata', conn, if_exists='replace', index=False) + + logger.info("Created metadata tables") + + def create_indexes(self, conn: sqlite3.Connection): + """Create database indexes for better query performance.""" + logger.info("Creating database indexes...") + + indexes = [ + "CREATE INDEX IF NOT EXISTS idx_training_unit ON training_data(unit_number)", + "CREATE INDEX IF NOT EXISTS idx_training_dataset ON training_data(dataset)", + "CREATE INDEX IF NOT EXISTS idx_training_cycle ON training_data(time_in_cycles)", + "CREATE INDEX IF NOT EXISTS idx_test_unit ON test_data(unit_number)", + "CREATE INDEX IF NOT EXISTS idx_test_dataset ON test_data(dataset)", + "CREATE INDEX IF NOT EXISTS idx_test_cycle ON test_data(time_in_cycles)", + "CREATE INDEX IF NOT EXISTS idx_rul_unit ON rul_data(unit_number, dataset)" + ] + + for index_sql in indexes: + conn.execute(index_sql) + + conn.commit() + logger.info("Created database indexes") + + def create_views(self, conn: sqlite3.Connection): + """Create useful database views for common queries.""" + logger.info("Creating database views...") + + # View for latest sensor readings per engine + latest_readings_view = """ + CREATE VIEW IF NOT EXISTS latest_sensor_readings AS + SELECT t1.* + FROM training_data t1 + INNER JOIN ( + SELECT unit_number, dataset, MAX(time_in_cycles) as max_cycle + FROM training_data + GROUP BY unit_number, dataset + ) t2 ON t1.unit_number = t2.unit_number + AND t1.dataset = t2.dataset + AND t1.time_in_cycles = t2.max_cycle + """ + + # View for engine health summary + engine_health_view = """ + CREATE VIEW IF NOT EXISTS engine_health_summary AS + SELECT + unit_number, + dataset, + MAX(time_in_cycles) as total_cycles, + MIN(RUL) as final_rul, + AVG(sensor_measurement_1) as avg_fan_inlet_temp, + AVG(sensor_measurement_11) as avg_hpc_outlet_pressure, + AVG(sensor_measurement_21) as avg_lpt_cool_air_flow + FROM training_data + GROUP BY unit_number, dataset + """ + + conn.execute(latest_readings_view) + conn.execute(engine_health_view) + conn.commit() + logger.info("Created database views") + + def validate_database(self, conn: sqlite3.Connection): + """Validate the created database by running sample queries.""" + logger.info("Validating database...") + + validation_queries = [ + ("Training data count", "SELECT COUNT(*) FROM training_data"), + ("Test data count", "SELECT COUNT(*) FROM test_data"), + ("RUL data count", "SELECT COUNT(*) FROM rul_data"), + ("Unique engines in training", "SELECT COUNT(DISTINCT unit_number) FROM training_data"), + ("Datasets available", "SELECT DISTINCT dataset FROM training_data"), + ] + + for description, query in validation_queries: + try: + result = conn.execute(query).fetchone() + logger.info(f"{description}: {result[0] if isinstance(result[0], (int, float)) else result}") + except Exception as e: + logger.error(f"Validation query failed - {description}: {e}") + + def process_dataset(self): + """Main method to process the entire NASA dataset.""" + logger.info(f"Starting NASA dataset processing...") + logger.info(f"Data directory: {self.data_dir.absolute()}") + logger.info(f"Database path: {self.db_path.absolute()}") + + # Check if data directory exists + if not self.data_dir.exists(): + logger.error(f"Data directory not found: {self.data_dir}") + logger.info("Please download the NASA Turbofan Engine Degradation Simulation Dataset") + logger.info("and place the text files in the 'data' directory") + return False + + try: + # Connect to SQLite database + with sqlite3.connect(self.db_path) as conn: + logger.info(f"Connected to database: {self.db_path}") + + # Process all data files + self.process_training_data(conn) + self.process_test_data(conn) + self.process_rul_data(conn) + self.create_metadata_tables(conn) + self.create_indexes(conn) + self.create_views(conn) + + # Validate the database + self.validate_database(conn) + + logger.info("Database processing completed successfully!") + return True + + except Exception as e: + logger.error(f"Error processing database: {e}") + return False + +def main(): + """Main function to run the database setup.""" + import argparse + + parser = argparse.ArgumentParser(description="Convert NASA Turbofan Dataset to SQLite") + parser.add_argument("--data-dir", default="data", + help="Directory containing NASA dataset text files") + parser.add_argument("--db-path", default="PredM_db/nasa_turbo.db", + help="Path for output SQLite database") + + args = parser.parse_args() + + processor = NASADatasetProcessor(args.data_dir, args.db_path) + success = processor.process_dataset() + + if success: + print(f"\n✅ Database created successfully at: {args.db_path}") + print("\nDatabase contains the following tables:") + print("- training_data: Engine run-to-failure trajectories") + print("- test_data: Engine test trajectories") + print("- rul_data: Ground truth RUL values") + print("- sensor_metadata: Sensor descriptions") + print("- dataset_metadata: Dataset information") + print("\nUseful views created:") + print("- latest_sensor_readings: Latest readings per engine") + print("- engine_health_summary: Engine health statistics") + else: + print("\n❌ Database creation failed. Check the logs above.") + return 1 + + return 0 + +if __name__ == "__main__": + exit(main()) \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/__init__.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/__init__.py new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/__init__.py @@ -0,0 +1 @@ + diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml new file mode 100644 index 000000000..8f12f674f --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml @@ -0,0 +1,94 @@ +general: + use_uvloop: true + telemetry: + logging: + console: + _type: console + level: DEBUG + tracing: + phoenix: + _type: phoenix + endpoint: http://localhost:6006/v1/traces + project: predictive-maintenance-app + +llms: + sql_llm: + _type: nim + model_name: "meta/llama-4-scout-17b-16e-instruct" + plotting_llm: + _type: nim + model_name: "meta/llama-4-scout-17b-16e-instruct" + nim_llm: + _type: nim + model_name: "meta/llama-4-scout-17b-16e-instruct" + reasoning_llm: + _type: nim + model_name: "deepseek-ai/deepseek-r1-distill-qwen-32b" + +embedders: + vanna_embedder: + _type: nim + model_name: "nvidia/nv-embed-v1" + +functions: + sql_retriever: + _type: generate_sql_query_and_retrieve_tool + llm_name: sql_llm + embedding_name: vanna_embedder + vector_store_path: "./database" + db_path: "./database/nasa_turbo.db" + output_folder: "./output_data" + predict_rul: + _type: predict_rul_tool + output_folder: "./output_data" + scaler_path: "./models/scaler_model.pkl" + model_path: "./models/xgb_model_fd001.pkl" + plot_distribution: + _type: plot_distribution_tool + output_folder: "./output_data" + plot_comparison: + _type: plot_comparison_tool + output_folder: "./output_data" + plot_line_chart: + _type: plot_line_chart_tool + output_folder: "./output_data" + plotting_agent: + _type: react_agent + llm_name: nim_llm + tool_names: [plot_line_chart, plot_comparison, plot_distribution] + verbose: true + handle_tool_errors: true + max_iterations: 1 + description: "Use this agent to generate plots from the retrieved SQL data. Provide a description of the plot required along with the JSON file path containing the SQL output in the input message. + for example: Plot the sensor_measurement_1 vs time_in_cycles for unit 1 from the file ./output_data/sql_output.json" + +workflow: + _type: react_agent + llm_name: nim_llm + tool_names: + - sql_retriever + - plotting_agent + - predict_rul + verbose: true + system_prompt: | + You are a conversational helpful assistant that can help with predictive maintenance tasks for a turbofan engine. + Answer the user's question as best as you can while not doing more than what is asked. You can optionally use the following tools to help with your task: + {tools} + Not all queries require you to use the tools, only use them if you need to. + If a tool returns an HTML file, send it to the user. + DO NOT GENERATE SQL QUERIES BY YOURSELF, ONLY USE THE TOOLS!!! + You may respond in one of two formats: + + Use the following format exactly when you want to use a tool: + + Question: the input question you must answer + Thought: you should always think about what to do + Action: the action to take, should be one of [{tool_names}] + Action Input: the input to the action (if there is no required input, include "Action Input: None") + Observation: wait for the tool to finish execution + + Use the following format exactly when you don't want to use a tool: + + Question: the input question you must answer + Thought: you should always think about what to do + Final Answer: the final answer to the original input question diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py new file mode 100644 index 000000000..0d0e8dff9 --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py @@ -0,0 +1,214 @@ +import json +import logging +import os + +from aiq.builder.framework_enum import LLMFrameworkEnum +from pydantic import Field + +from aiq.builder.builder import Builder +from aiq.builder.function_info import FunctionInfo +from aiq.cli.register_workflow import register_function +from aiq.data_models.function import FunctionBaseConfig + +logger = logging.getLogger(__name__) + +class GenerateSqlQueryAndRetrieveToolConfig(FunctionBaseConfig, name="generate_sql_query_and_retrieve_tool"): + """ + AIQ Toolkit function to generate SQL queries and retrieve data. + """ + # Add your custom configuration parameters here + llm_name: str = Field(description="The name of the LLM to use for the function.") + embedding_name: str = Field(description="The name of the embedding to use for the function.") + vector_store_path: str = Field(description="The path to the vector store to use for the function.") + db_path: str = Field(description="The path to the SQL database to use for the function.") + output_folder: str = Field(description="The path to the output folder to use for the function.") + +@register_function(config_type=GenerateSqlQueryAndRetrieveToolConfig) +async def generate_sql_query_and_retrieve_tool( + config: GenerateSqlQueryAndRetrieveToolConfig, builder: Builder +): + """ + Generate a SQL query for a given question and retrieve the data from the database. + """ + # Create Vanna instance + vanna_llm_config = builder.get_llm_config(config.llm_name) + vanna_embedder_config = builder.get_embedder_config(config.embedding_name) + + from langchain_core.prompts.chat import ChatPromptTemplate + + llm = await builder.get_llm(config.llm_name, wrapper_type=LLMFrameworkEnum.LANGCHAIN) + + system_prompt = """ + You are an intelligent SQL query assistant that analyzes database query results and provides appropriate responses. + + Your responsibilities: + 1. Analyze the SQL query results and determine the best response format + 2. For data extraction queries (multiple rows/complex data): recommend saving to JSON file and provide summary + 3. For simple queries (single values, counts, yes/no): provide direct answers without file storage + 4. Always be helpful and provide context about the results + + Guidelines: + - If results contain multiple rows or complex data (>5 rows or >3 columns): recommend saving to file + - If results are simple (single value, count, or small lookup): provide direct answer + - Always mention the SQL query that was executed + - Be clear about whether data was saved to a file or not + """ + + user_prompt = """ + Original Question: {original_question} + + SQL Query Executed: {sql_query} + + Query Results: + - Number of rows: {num_rows} + - Number of columns: {num_columns} + - Columns: {columns} + - Sample data (first few rows): {sample_data} + + File Path (if data should be saved): {file_path} + + Please provide an appropriate response that either: + 1. Saves the data to JSON file and provides a summary (for complex/large datasets) + 2. Directly answers the question with the results (for simple queries) + + Be conversational and helpful. Explain what was found and next steps if applicable. + """ + + prompt = ChatPromptTemplate.from_messages([("system", system_prompt), ("user", user_prompt)]) + output_message = prompt | llm + + from .vanna_util import NIMVanna, initVanna, CustomEmbeddingFunction + def get_vanna_instance(vanna_llm_config, vanna_embedder_config, vector_store_path, db_path): + """ + Get a Vanna instance for the given configuration. + Initializes the Vanna instance if it is not already initialized. + + Args: + vanna_llm_config (dict): The configuration for the Vanna LLM. + vanna_embedder_config (dict): The configuration for the Vanna embedder. + vector_store_path (str): The path to the vector store. + db_path (str): The path to the SQL database. + + Returns: + NIMVanna: A Vanna instance. + """ + vn_instance = NIMVanna( + VectorConfig={ + "client": "persistent", + "path": vector_store_path, + "embedding_function": CustomEmbeddingFunction( + api_key=os.getenv("NVIDIA_API_KEY"), + model=vanna_embedder_config.model_name) + }, + LLMConfig={ + "api_key": os.getenv("NVIDIA_API_KEY"), + "model": vanna_llm_config.model_name + } + ) + + # Connect to SQLite database + vn_instance.connect_to_sqlite(db_path) + + # Check if vector store directory is empty and initialize if needed + list_of_folders = [d for d in os.listdir(vector_store_path) + if os.path.isdir(os.path.join(vector_store_path, d))] + if len(list_of_folders) == 0: + logger.info("Initializing Vanna vector store...") + try: + initVanna(vn_instance) + logger.info("Vanna vector store initialization complete.") + except Exception as e: + logger.error(f"Error initializing Vanna vector store: {e}") + raise + else: + logger.info("Vanna vector store already initialized.") + return vn_instance + + vn_instance = get_vanna_instance(vanna_llm_config, vanna_embedder_config, config.vector_store_path, config.db_path) + + async def _response_fn(input_message: str) -> str: + # Process the input_message and generate output + if vn_instance is None: + return "Error: Vanna instance not available" + + sql = None + try: + sql = vn_instance.generate_sql(question=input_message) + logger.info(f"Generated SQL: {sql}") + except Exception as e: + return f"Error generating SQL: {e}" + + if not vn_instance.run_sql_is_set: + return f"Database is not connected via Vanna: {sql}" + + try: + df = vn_instance.run_sql(sql) + if df is None: + return f"Vanna run_sql returned None: {sql}" + if df.empty: + return f"No data found for the generated SQL: {sql}" + + num_rows = df.shape[0] + num_columns = df.shape[1] + columns = df.columns.tolist() + + # Get sample data (first 3 rows for preview) + sample_data = df.head(3).to_dict('records') + + # Prepare file path for potential saving + sql_output_path = os.path.join(config.output_folder, "sql_output.json") + + # Use LLM to generate intelligent response + response = await output_message.ainvoke({ + "original_question": input_message, + "sql_query": sql, + "num_rows": num_rows, + "num_columns": num_columns, + "columns": ", ".join(columns), + "sample_data": json.dumps(sample_data, indent=2), + "file_path": sql_output_path + }) + + # Check if LLM response suggests saving data (look for keywords or patterns) + llm_response = response.content if hasattr(response, 'content') else str(response) + + # Save data if it's complex (multiple rows or columns) or LLM suggests saving + should_save_data = ( + num_rows > 5 or + num_columns > 3 or + "save" in llm_response.lower() or + "saved" in llm_response.lower() or + "file" in llm_response.lower() + ) + + if should_save_data: + # Save the data to JSON file + os.makedirs(config.output_folder, exist_ok=True) + json_result = df.to_json(orient="records") + with open(sql_output_path, 'w') as f: + json.dump(json.loads(json_result), f, indent=4) + logger.info(f"Data saved to {sql_output_path}") + + # If LLM didn't mention saving, append save confirmation + if "saved" not in llm_response.lower(): + llm_response += f"\n\n📁 Data has been saved to: {sql_output_path} with the following columns: {', '.join(columns)}" + + return llm_response + + except Exception as e: + return f"Error running SQL query '{sql}': {e}" + + description = """ + Use this tool to automatically generate SQL queries for the user's question, retrieve the data from the SQL database and store the data in a JSON file or provide a summary of the data. + Do not provide SQL query as input, only a question in plain english. + Input: User's question or a question that you think is relevant to the user's question in plain english. + Output: Status of the generated SQL query's execution. + """ + yield FunctionInfo.from_fn(_response_fn, + description=description) + try: + pass + except GeneratorExit: + logger.info("Generate SQL query and retrieve function exited early!") + finally: + logger.info("Cleaning up generate_sql_query_and_retrieve_tool workflow.") \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py new file mode 100644 index 000000000..5f272cc30 --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py @@ -0,0 +1,360 @@ +import json +import logging +import os +import pandas as pd + +from pydantic import Field + +from aiq.builder.builder import Builder +from aiq.builder.function_info import FunctionInfo +from aiq.cli.register_workflow import register_function +from aiq.data_models.function import FunctionBaseConfig + +logger = logging.getLogger(__name__) + +def verify_json_path(file_path: str) -> str: + """ + Verify that the input is a valid path to a JSON file. + + Args: + file_path (str): Path to verify + + Returns: + str: Verified file path + + Raises: + ValueError: If input is not a string or not a JSON file + FileNotFoundError: If file does not exist + json.JSONDecodeError: If file contains invalid JSON + """ + if not isinstance(file_path, str): + raise ValueError("Input must be a string path to a JSON file") + + if not file_path.lower().endswith('.json'): + raise ValueError("Input must be a path to a JSON file (ending with .json)") + + if not os.path.exists(file_path): + raise FileNotFoundError(f"JSON file not found at path: {file_path}") + + try: + with open(file_path, 'r') as f: + json.load(f) # Verify file contains valid JSON + except json.JSONDecodeError: + raise ValueError(f"File at {file_path} does not contain valid JSON data") + + return file_path + +def knee_RUL(cycle_list, max_cycle, MAXLIFE): + ''' + Piecewise linear function with zero gradient and unit gradient + ^ + | + MAXLIFE |----------- + | \ + | \ + | \ + | \ + | \ + |-----------------------> + ''' + knee_RUL_values = [] + if max_cycle >= MAXLIFE: + knee_point = max_cycle - MAXLIFE + + for i in range(0, len(cycle_list)): + if i < knee_point: + knee_RUL_values.append(MAXLIFE) + else: + tmp = knee_RUL_values[i - 1] - (MAXLIFE / (max_cycle - knee_point)) + knee_RUL_values.append(tmp) + else: + knee_point = MAXLIFE + print("=========== knee_point < MAXLIFE ===========") + for i in range(0, len(cycle_list)): + knee_point -= 1 + knee_RUL_values.append(knee_point) + + return knee_RUL_values + +def apply_piecewise_rul_to_data(df, cycle_col='time_in_cycles', max_life=125): + """ + Apply piecewise RUL transformation to single-engine data. + Uses original RUL values to determine proper failure point. + + Args: + df (pd.DataFrame): Input dataframe (single engine) + cycle_col (str): Column name for cycle/time + max_life (int): Maximum life parameter for knee_RUL function + + Returns: + pd.DataFrame: DataFrame with transformed RUL column + """ + df_copy = df.copy() + + # Check if cycle column exists + if cycle_col not in df_copy.columns: + logger.warning(f"Cycle column '{cycle_col}' not found. Using row index as cycle.") + df_copy[cycle_col] = range(1, len(df_copy) + 1) + + # Get cycle list for single engine + cycle_list = df_copy[cycle_col].tolist() + max_cycle_in_data = max(cycle_list) + + # Use original RUL values to determine true failure point + # Following the original GitHub pattern: max_cycle = max(cycle_list) + final_rul + # Get the final RUL value (RUL at the last cycle in our data) + final_rul = df_copy.loc[df_copy[cycle_col] == max_cycle_in_data, 'actual_RUL'].iloc[0] + # True failure point = last cycle in data + remaining RUL + true_max_cycle = max_cycle_in_data + final_rul + logger.info(f"Using original RUL data: final_rul={final_rul}, true_failure_cycle={true_max_cycle}") + + # Apply knee_RUL function with the true failure point + rul_values = knee_RUL(cycle_list, true_max_cycle, max_life) + + # Replace actual_RUL column with piecewise values + df_copy['actual_RUL'] = rul_values + + return df_copy + +class PlotComparisonToolConfig(FunctionBaseConfig, name="plot_comparison_tool"): + """ + AIQ Toolkit function to plot comparison of two y-axis columns against an x-axis column. + """ + # Configuration parameters + # x_axis_column: str = Field(description="The column name for x-axis data.", default="time_in_cycles") + # y_axis_column_1: str = Field(description="The first column name for y-axis data.", default="actual_RUL") + # y_axis_column_2: str = Field(description="The second column name for y-axis data.", default="predicted_RUL") + # plot_title: str = Field(description="The title for the plot.", default="Comparison Plot") + output_folder: str = Field(description="The path to the output folder to save plots.", default="./output_data") + +@register_function(config_type=PlotComparisonToolConfig) +async def plot_comparison_tool( + config: PlotComparisonToolConfig, builder: Builder +): + def load_data_from_json(json_path: str): + """Load data from JSON file into a pandas DataFrame.""" + import pandas as pd + try: + with open(json_path, 'r') as f: + data = json.load(f) + return pd.DataFrame(data) + except FileNotFoundError: + logger.error(f"JSON file not found at {json_path}") + return None + except json.JSONDecodeError: + logger.error(f"Could not decode JSON from {json_path}") + return None + except Exception as e: + logger.error(f"Error loading data from '{json_path}': {e}") + return None + + def create_comparison_plot_html(output_dir: str, data_json_path: str, x_col: str, y_col_1: str, y_col_2: str, title: str): + """ + Generate and save comparison plot as HTML file using Bokeh. + + Args: + output_dir (str): Directory to save the plot file. + data_json_path (str): Path to the input JSON data file. + x_col (str): Column name for x-axis. + y_col_1 (str): Column name for first y-axis line. + y_col_2 (str): Column name for second y-axis line. + title (str): Plot title. + + Returns: + str: Path to the saved HTML file. + """ + import bokeh.plotting as bp + from bokeh.models import ColumnDataSource, HoverTool, Legend + from bokeh.embed import file_html + from bokeh.resources import CDN + + df = load_data_from_json(data_json_path) + if df is None or df.empty: + raise ValueError(f"Could not load data or data is empty from {data_json_path}") + + # Check required columns + required_columns = [x_col, y_col_1, y_col_2] + missing_columns = [col for col in required_columns if col not in df.columns] + if missing_columns: + raise KeyError(f"Data from {data_json_path} must contain columns: {required_columns}. Missing: {missing_columns}") + + # Apply piecewise RUL transformation if any column is "actual_RUL" + rul_transformation_applied = False + if y_col_1 == "actual_RUL" or y_col_2 == "actual_RUL": + logger.info("Applying piecewise RUL transformation...") + df = apply_piecewise_rul_to_data(df, x_col) + rul_transformation_applied = True + logger.info("Piecewise RUL transformation completed") + + # Sort by x-axis column for proper line plotting + df_sorted = df.sort_values(x_col) + + # Create data sources for each line + line1_source = ColumnDataSource(data=dict( + x=df_sorted[x_col], + y=df_sorted[y_col_1], + label=[y_col_1] * len(df_sorted) + )) + + line2_source = ColumnDataSource(data=dict( + x=df_sorted[x_col], + y=df_sorted[y_col_2], + label=[y_col_2] * len(df_sorted) + )) + + # Create hover tools + hover_line1 = HoverTool(tooltips=[ + (f"{x_col}", "@x"), + (f"{y_col_1}", "@y{0.0}"), + ("Type", "@label") + ], renderers=[]) + + hover_line2 = HoverTool(tooltips=[ + (f"{x_col}", "@x"), + (f"{y_col_2}", "@y{0.0}"), + ("Type", "@label") + ], renderers=[]) + + fig = bp.figure( + title=title, + x_axis_label=x_col, + y_axis_label='Value', + width=800, # Increased width to provide more space + height=450, + tools=['pan', 'box_zoom', 'wheel_zoom', 'reset', 'save'], + toolbar_location="above" + ) + + # Add the lines + line2_render = fig.line( + 'x', 'y', source=line2_source, + line_color='#2E8B57', line_width=3, alpha=0.9, + legend_label=y_col_2 + ) + + line1_render = fig.line( + 'x', 'y', source=line1_source, + line_color='#20B2AA', line_width=3, alpha=0.9, + line_dash='dashed', legend_label=y_col_1 + (" (Piecewise)" if y_col_1 == "actual_RUL" and rul_transformation_applied else "") + ) + + # Add hover tools to specific renderers + hover_line2.renderers = [line2_render] + hover_line1.renderers = [line1_render] + fig.add_tools(hover_line2, hover_line1) + + # Style the plot + fig.title.text_font_size = "16pt" + fig.title.align = "center" + fig.xaxis.axis_label_text_font_size = "14pt" + fig.yaxis.axis_label_text_font_size = "14pt" + + # Position legend in bottom right and style it + fig.legend.location = "bottom_right" + fig.legend.label_text_font_size = "12pt" + fig.legend.glyph_width = 30 + fig.legend.background_fill_alpha = 0.8 + fig.legend.background_fill_color = "white" + fig.legend.border_line_color = "gray" + fig.legend.border_line_width = 1 + fig.legend.padding = 10 + + fig.grid.grid_line_alpha = 0.3 + + # Set axis ranges for better visualization + y_min = min(df_sorted[y_col_1].min(), df_sorted[y_col_2].min()) + y_max = max(df_sorted[y_col_1].max(), df_sorted[y_col_2].max()) + y_range = y_max - y_min + fig.y_range.start = max(0, y_min - y_range * 0.05) + fig.y_range.end = y_max + y_range * 0.05 + + # Generate standalone HTML file + html_content = file_html(fig, CDN, title) + + # Save the HTML file + os.makedirs(output_dir, exist_ok=True) + output_filepath = os.path.join(output_dir, f"comparison_plot_{y_col_1}_vs_{y_col_2}.html") + with open(output_filepath, 'w', encoding='utf-8') as f: + f.write(html_content) + logger.info(f"Comparison plot saved to {output_filepath}") + + return output_filepath + + async def _response_fn(data_json_path: str, x_axis_column: str, y_axis_column_1: str, y_axis_column_2: str, plot_title: str) -> str: + """ + Process the input message and generate comparison plot. + """ + try: + # Load data to validate columns exist + df = load_data_from_json(data_json_path) + if df is None or df.empty: + return "Could not load data or data is empty from the provided JSON file" + + # Check required columns + required_columns = [x_axis_column, y_axis_column_1, y_axis_column_2] + missing_columns = [col for col in required_columns if col not in df.columns] + if missing_columns: + return f"Data from {data_json_path} must contain columns: {required_columns}. Missing: {missing_columns}" + + output_filepath = create_comparison_plot_html( + output_dir=config.output_folder, + data_json_path=data_json_path, + x_col=x_axis_column, + y_col_1=y_axis_column_1, + y_col_2=y_axis_column_2, + title=plot_title + ) + + # Convert absolute path to file:// URL for proper browser handling + file_url = f"file://{output_filepath}" + + # Add info about RUL transformation if applied + rul_info = "" + if y_axis_column_1 == "RUL" or y_axis_column_2 == "RUL": + rul_info = f"\n- Piecewise RUL transformation applied (max_life=125)" + + # Return a clear completion message that the LLM will understand + return f""" + TASK COMPLETED SUCCESSFULLY\n\nComparison plot has been generated and saved. + \n\nChart Details:\n- + Type: Comparison plot with two lines\n- X-axis: {x_axis_column}\n- Y-axis Line 1: {y_axis_column_1} (dashed teal)\n- Y-axis Line 2: {y_axis_column_2} (solid green)\n- Title: {plot_title}{rul_info}\n- Output File: {output_filepath}\n- File URL: {file_url}\n\n✅ CHART GENERATION COMPLETE - NO FURTHER ACTION NEEDED + """ + + except FileNotFoundError as e: + error_msg = f"Required data file ('{data_json_path}') not found for comparison plot: {e}" + logger.error(error_msg) + return error_msg + except KeyError as ke: + error_msg = f"Missing required columns in '{data_json_path}' for comparison plot: {ke}" + logger.error(error_msg) + return error_msg + except ValueError as ve: + error_msg = f"Data validation error for comparison plot: {ve}" + logger.error(error_msg) + return error_msg + except Exception as e: + error_msg = f"Error generating comparison plot: {e}" + logger.error(error_msg) + return error_msg + + prompt = """ + Generate interactive comparison plot between two columns from JSON data. + + Input: + - data_json_path: Path to the JSON file containing the data + - x_axis_column: Column name for x-axis data + - y_axis_column_1: Column name for first y-axis data + - y_axis_column_2: Column name for second y-axis data + - plot_title: Title for the plot + + Output: + - HTML file containing the comparison plot + """ + yield FunctionInfo.from_fn(_response_fn, + description=prompt) + try: + pass + except GeneratorExit: + logger.info("Plot comparison function exited early!") + finally: + logger.info("Cleaning up plot_comparison_tool workflow.") \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py new file mode 100644 index 000000000..e4499e44c --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py @@ -0,0 +1,209 @@ +import json +import logging +import os + +from pydantic import Field + +from aiq.builder.builder import Builder +from aiq.builder.function_info import FunctionInfo +from aiq.cli.register_workflow import register_function +from aiq.data_models.function import FunctionBaseConfig + +logger = logging.getLogger(__name__) + +def verify_json_path(file_path: str) -> str: + """ + Verify that the input is a valid path to a JSON file. + + Args: + file_path (str): Path to verify + + Returns: + str: Verified file path + + Raises: + ValueError: If input is not a string or not a JSON file + FileNotFoundError: If file does not exist + json.JSONDecodeError: If file contains invalid JSON + """ + if not isinstance(file_path, str): + raise ValueError("Input must be a string path to a JSON file") + + if not file_path.lower().endswith('.json'): + raise ValueError("Input must be a path to a JSON file (ending with .json)") + + if not os.path.exists(file_path): + raise FileNotFoundError(f"JSON file not found at path: {file_path}") + + try: + with open(file_path, 'r') as f: + json.load(f) # Verify file contains valid JSON + except json.JSONDecodeError: + raise ValueError(f"File at {file_path} does not contain valid JSON data") + + return file_path + +class PlotDistributionToolConfig(FunctionBaseConfig, name="plot_distribution_tool"): + """ + AIQ Toolkit function to plot distribution histogram of a specified column. + """ + # Configuration parameters + data_json_path: str = Field(description="The path to the JSON file containing the data.", default="") + column_name: str = Field(description="The column name to create distribution plot for.", default="RUL") + plot_title: str = Field(description="The title for the plot.", default="Distribution Plot") + output_folder: str = Field(description="The path to the output folder to save plots.", default="./output_data") + +@register_function(config_type=PlotDistributionToolConfig) +async def plot_distribution_tool( + config: PlotDistributionToolConfig, builder: Builder +): + def load_data_from_json(json_path: str): + """Load data from JSON file into a pandas DataFrame.""" + import pandas as pd + try: + with open(json_path, 'r') as f: + data = json.load(f) + return pd.DataFrame(data) + except FileNotFoundError: + logger.error(f"JSON file not found at {json_path}") + return None + except json.JSONDecodeError: + logger.error(f"Could not decode JSON from {json_path}") + return None + except Exception as e: + logger.error(f"Error loading data from '{json_path}': {e}") + return None + + def create_distribution_plot_html(output_dir: str, data_json_path: str, column_name: str, title: str): + """ + Generate and save distribution histogram as HTML file using Bokeh. + + Args: + output_dir (str): Directory to save the plot file. + data_json_path (str): Path to the input JSON data file. + column_name (str): Column name to create distribution for. + title (str): Plot title. + + Returns: + str: Path to the saved HTML file. + """ + import bokeh.plotting as bp + from bokeh.models import ColumnDataSource, HoverTool + from bokeh.embed import file_html + from bokeh.resources import CDN + import pandas as pd + import numpy as np + + df = load_data_from_json(data_json_path) + if df is None or df.empty: + raise ValueError(f"Could not load data or data is empty from {data_json_path}") + + if column_name not in df.columns: + raise KeyError(f"Data from {data_json_path} must contain '{column_name}' column. Found: {df.columns.tolist()}") + + # Create histogram data + hist, edges = np.histogram(df[column_name], bins=30) + hist_df = pd.DataFrame({ + f'{column_name}_left': edges[:-1], + f'{column_name}_right': edges[1:], + 'Frequency': hist + }) + + source = ColumnDataSource(hist_df) + hover = HoverTool(tooltips=[ + (f"{column_name} Range", f"@{column_name}_left{{0.0}} - @{column_name}_right{{0.0}}"), + ("Frequency", "@Frequency") + ]) + + fig = bp.figure( + title=title, + x_axis_label=column_name, + y_axis_label='Frequency', + width=650, + height=450, + tools=[hover, 'pan', 'box_zoom', 'wheel_zoom', 'reset', 'save'], + toolbar_location="above" + ) + + # Add the histogram bars + fig.quad( + bottom=0, top='Frequency', left=f'{column_name}_left', right=f'{column_name}_right', + fill_color='#e17160', line_color='white', alpha=0.8, source=source + ) + + # Style the plot + fig.title.text_font_size = "14pt" + fig.xaxis.axis_label_text_font_size = "12pt" + fig.yaxis.axis_label_text_font_size = "12pt" + fig.grid.grid_line_alpha = 0.3 + + # Generate standalone HTML file + html_content = file_html(fig, CDN, title) + + # Save the HTML file + os.makedirs(output_dir, exist_ok=True) + output_filepath = os.path.join(output_dir, f"distribution_plot_{column_name}.html") + with open(output_filepath, 'w', encoding='utf-8') as f: + f.write(html_content) + logger.info(f"Distribution plot saved to {output_filepath}") + + return output_filepath + + async def _response_fn(data_json_path: str, column_name: str, plot_title: str) -> str: + """ + Process the input message and generate distribution histogram file. + """ + data_json_path = verify_json_path(data_json_path) + try: + # Load data to validate column exists + df = load_data_from_json(data_json_path) + if df is None or df.empty: + return "Could not load data or data is empty from the provided JSON file" + + if config.column_name not in df.columns: + return f"Column '{config.column_name}' not found in data. Available columns: {df.columns.tolist()}" + + output_filepath = create_distribution_plot_html( + output_dir=config.output_folder, + data_json_path=data_json_path, + column_name=column_name, + title=plot_title + ) + + # Convert absolute path to file:// URL for proper browser handling + file_url = f"file://{output_filepath}" + + # Return a clear completion message that the LLM will understand + return f"TASK COMPLETED SUCCESSFULLY\n\nDistribution histogram has been generated and saved.\n\nChart Details:\n- Type: Distribution histogram (30 bins)\n- Column: {column_name}\n- Title: {plot_title}\n- Output File: {output_filepath}\n- File URL: {file_url}\n\n✅ CHART GENERATION COMPLETE - NO FURTHER ACTION NEEDED" + + except FileNotFoundError as e: + error_msg = f"Required data file ('{data_json_path}') not found for distribution plot: {e}" + logger.error(error_msg) + return error_msg + except KeyError as ke: + error_msg = f"Missing expected column '{column_name}' in '{data_json_path}' for distribution plot: {ke}" + logger.error(error_msg) + return error_msg + except Exception as e: + error_msg = f"Error generating distribution histogram: {e}" + logger.error(error_msg) + return error_msg + + prompt = """ + Generate interactive distribution histogram from JSON data. + Input: + - data_json_path: Path to the JSON file containing the data + - column_name: Column name for the distribution histogram + - plot_title: Title for the plot + + Output: + - HTML file containing the distribution histogram + """ + yield FunctionInfo.from_fn(_response_fn, + description=prompt) + try: + pass + except GeneratorExit: + logger.info("Plot distribution function exited early!") + finally: + logger.info("Cleaning up plot_distribution_tool workflow.") \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py new file mode 100644 index 000000000..2f324f8a7 --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py @@ -0,0 +1,240 @@ +import json +import logging +import os + +from pydantic import Field + +from aiq.builder.builder import Builder +from aiq.builder.function_info import FunctionInfo +from aiq.cli.register_workflow import register_function +from aiq.data_models.function import FunctionBaseConfig + +logger = logging.getLogger(__name__) + +def verify_json_path(file_path: str) -> str: + """ + Verify that the input is a valid path to a JSON file. + + Args: + file_path (str): Path to verify + + Returns: + str: Verified file path + + Raises: + ValueError: If input is not a string or not a JSON file + FileNotFoundError: If file does not exist + json.JSONDecodeError: If file contains invalid JSON + """ + if not isinstance(file_path, str): + raise ValueError("Input must be a string path to a JSON file") + + if not file_path.lower().endswith('.json'): + raise ValueError("Input must be a path to a JSON file (ending with .json)") + + if not os.path.exists(file_path): + raise FileNotFoundError(f"JSON file not found at path: {file_path}") + + try: + with open(file_path, 'r') as f: + json.load(f) # Verify file contains valid JSON + except json.JSONDecodeError: + raise ValueError(f"File at {file_path} does not contain valid JSON data") + + return file_path + +class PlotLineChartToolConfig(FunctionBaseConfig, name="plot_line_chart_tool"): + """ + AIQ Toolkit function to plot a line chart with specified x and y axis columns. + """ + # Configuration parameters + # data_json_path: str = Field(description="The path to the JSON file containing the data.", default="./output_data/sql_output.json") + # x_axis_column: str = Field(description="The column name for x-axis data.", default="time_in_cycles") + # y_axis_column: str = Field(description="The column name for y-axis data.", default="RUL") + # plot_title: str = Field(description="The title for the plot.", default="Line Chart") + output_folder: str = Field(description="The path to the output folder to save plots.", default="./output_data") + +@register_function(config_type=PlotLineChartToolConfig) +async def plot_line_chart_tool( + config: PlotLineChartToolConfig, builder: Builder +): + def load_data_from_json(json_path: str): + """Load data from JSON file into a pandas DataFrame.""" + import pandas as pd + try: + with open(json_path, 'r') as f: + data = json.load(f) + return pd.DataFrame(data) + except FileNotFoundError: + logger.error(f"JSON file not found at {json_path}") + return None + except json.JSONDecodeError: + logger.error(f"Could not decode JSON from {json_path}") + return None + except Exception as e: + logger.error(f"Error loading data from '{json_path}': {e}") + return None + + def create_line_chart_plot_html(output_dir: str, data_json_path: str, x_col: str, y_col: str, title: str): + """ + Generate and save line chart as HTML file using Bokeh. + + Args: + output_dir (str): Directory to save the plot file. + data_json_path (str): Path to the input JSON data file. + x_col (str): Column name for x-axis. + y_col (str): Column name for y-axis. + title (str): Plot title. + + Returns: + str: Path to the saved HTML file. + """ + import bokeh.plotting as bp + from bokeh.models import ColumnDataSource, HoverTool + from bokeh.embed import file_html + from bokeh.resources import CDN + import pandas as pd + + df = load_data_from_json(data_json_path) + if df is None or df.empty: + raise ValueError(f"Could not load data or data is empty from {data_json_path}") + + # Check required columns + required_columns = [x_col, y_col] + missing_columns = [col for col in required_columns if col not in df.columns] + if missing_columns: + raise KeyError(f"Data from {data_json_path} must contain columns: {required_columns}. Missing: {missing_columns}") + + # Sort by x-axis column for proper line plotting + df_sorted = df.sort_values(x_col) + + # Create data source + source = ColumnDataSource(data=dict( + x=df_sorted[x_col], + y=df_sorted[y_col] + )) + + # Create hover tool + hover = HoverTool(tooltips=[ + (f"{x_col}", "@x"), + (f"{y_col}", "@y{0.00}") + ]) + + fig = bp.figure( + title=title, + x_axis_label=x_col, + y_axis_label=y_col, + width=650, + height=450, + tools=[hover, 'pan', 'box_zoom', 'wheel_zoom', 'reset', 'save'], + toolbar_location="above" + ) + + # Add the line + fig.line( + 'x', 'y', source=source, + line_color='#1f77b4', line_width=3, alpha=0.9 + ) + + # Add circle markers for data points + fig.circle( + 'x', 'y', source=source, + size=6, color='#1f77b4', alpha=0.7 + ) + + # Style the plot + fig.title.text_font_size = "16pt" + fig.title.align = "center" + fig.xaxis.axis_label_text_font_size = "14pt" + fig.yaxis.axis_label_text_font_size = "14pt" + fig.grid.grid_line_alpha = 0.3 + + # Set axis ranges for better visualization + y_min = df_sorted[y_col].min() + y_max = df_sorted[y_col].max() + y_range = y_max - y_min + if y_range > 0: + fig.y_range.start = y_min - y_range * 0.05 + fig.y_range.end = y_max + y_range * 0.05 + + # Generate standalone HTML file + html_content = file_html(fig, CDN, title) + + # Save the HTML file + os.makedirs(output_dir, exist_ok=True) + output_filepath = os.path.join(output_dir, f"line_chart_{x_col}_vs_{y_col}.html") + with open(output_filepath, 'w', encoding='utf-8') as f: + f.write(html_content) + logger.info(f"Line chart saved to {output_filepath}") + + return output_filepath + + async def _response_fn(data_json_path: str, x_axis_column: str, y_axis_column: str, plot_title: str) -> str: + """ + Process the input message and generate line chart. + """ + data_json_path = verify_json_path(data_json_path) + + try: + # Load data to validate columns exist + df = load_data_from_json(data_json_path) + if df is None or df.empty: + return "Could not load data or data is empty from the provided JSON file" + + # Check required columns + required_columns = [x_axis_column, y_axis_column] + missing_columns = [col for col in required_columns if col not in df.columns] + if missing_columns: + return f"Data from {data_json_path} must contain columns: {required_columns}. Missing: {missing_columns}" + + output_filepath = create_line_chart_plot_html( + output_dir=config.output_folder, + data_json_path=data_json_path, + x_col=x_axis_column, + y_col=y_axis_column, + title=plot_title + ) + + # Convert absolute path to file:// URL for proper browser handling + file_url = f"file://{output_filepath}" + + # Return a clear completion message that the LLM will understand + return f"TASK COMPLETED SUCCESSFULLY\n\nLine chart has been generated and saved.\n\nChart Details:\n- Type: Line chart with markers\n- X-axis: {x_axis_column}\n- Y-axis: {y_axis_column}\n- Title: {plot_title}\n- Output File: {output_filepath}\n- File URL: {file_url}\n\n✅ CHART GENERATION COMPLETE - NO FURTHER ACTION NEEDED" + + except FileNotFoundError as e: + error_msg = f"Required data file ('{data_json_path}') not found for line chart: {e}" + logger.error(error_msg) + return error_msg + except KeyError as ke: + error_msg = f"Missing required columns in '{data_json_path}' for line chart: {ke}" + logger.error(error_msg) + return error_msg + except ValueError as ve: + error_msg = f"Data validation error for line chart: {ve}" + logger.error(error_msg) + return error_msg + except Exception as e: + error_msg = f"Error generating line chart: {e}" + logger.error(error_msg) + return error_msg + + prompt = """ + Generate interactive line chart from JSON data. + + Input: + - data_json_path: Path to the JSON file containing the data + - x_axis_column: Column name for x-axis data + - y_axis_column: Column name for y-axis data + - plot_title: Title for the plot + + Output: + - HTML file containing the line chart + """ + yield FunctionInfo.from_fn(_response_fn, + description=prompt) + try: + pass + except GeneratorExit: + logger.info("Plot line chart function exited early!") + finally: + logger.info("Cleaning up plot_line_chart_tool workflow.") \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py new file mode 100644 index 000000000..9d3cbb69f --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py @@ -0,0 +1,247 @@ +import json +import logging +import os +import warnings +import pickle +import joblib +import numpy as np + +from pydantic import Field + +from aiq.builder.builder import Builder +from aiq.builder.function_info import FunctionInfo +from aiq.cli.register_workflow import register_function +from aiq.data_models.function import FunctionBaseConfig + +logger = logging.getLogger(__name__) + +def verify_json_path(file_path: str) -> str: + """ + Verify that the input is a valid path to a JSON file. + + Args: + file_path (str): Path to verify + + Returns: + str: Verified file path + + Raises: + ValueError: If input is not a string or not a JSON file + FileNotFoundError: If file does not exist + json.JSONDecodeError: If file contains invalid JSON + """ + if not isinstance(file_path, str): + return "Input must be a string path to a JSON file" + + if not file_path.lower().endswith('.json'): + return "Input must be a path to a JSON file (ending with .json)" + + if not os.path.exists(file_path): + return f"JSON file not found at path: {file_path}" + + try: + with open(file_path, 'r') as f: + json.load(f) # Verify file contains valid JSON + except json.JSONDecodeError: + return f"File at {file_path} does not contain valid JSON data" + + return file_path + +class PredictRulToolConfig(FunctionBaseConfig, name="predict_rul_tool"): + """ + AIQ Toolkit function to predict RUL (Remaining Useful Life) using trained models and provided data. + """ + # Configuration parameters + scaler_path: str = Field(description="Path to the trained StandardScaler model.", default="./models/scaler_model.pkl") + model_path: str = Field(description="Path to the trained XGBoost model.", default="./models/xgb_model_fd001.pkl") + output_folder: str = Field(description="The path to the output folder to save prediction results.", default="./output_data") + +@register_function(config_type=PredictRulToolConfig) +async def predict_rul_tool( + config: PredictRulToolConfig, builder: Builder +): + def load_data_from_json(json_path: str): + """Load data from JSON file into a pandas DataFrame.""" + import pandas as pd + try: + with open(json_path, 'r') as f: + data = json.load(f) + return pd.DataFrame(data) + except FileNotFoundError: + logger.warn(f"JSON file not found at {json_path}") + return None + except json.JSONDecodeError: + logger.warn(f"Could not decode JSON from {json_path}") + return None + except Exception as e: + logger.warn(f"Error loading data from '{json_path}': {e}") + return None + + def predict_rul_from_data(data_json_path: str, scaler_path: str, model_path: str, output_dir: str): + """ + Load data and trained models to make RUL predictions. + + Args: + data_json_path (str): Path to the input JSON data file. + scaler_path (str): Path to the trained StandardScaler model. + model_path (str): Path to the trained XGBoost model. + output_dir (str): Directory to save prediction results (unused - kept for compatibility). + + Returns: + tuple: (predictions array, original file path) + """ + import pandas as pd + + # Suppress warnings + warnings.filterwarnings("ignore", message="X does not have valid feature names") + + # Load the data + df = load_data_from_json(data_json_path) + if df is None or df.empty: + raise ValueError(f"Could not load data or data is empty from {data_json_path}") + + # Prepare features for prediction (exclude non-feature columns if present) + required_columns = ['sensor_measurement_2', + 'sensor_measurement_3', + 'sensor_measurement_4', + 'sensor_measurement_7', + 'sensor_measurement_8', + 'sensor_measurement_11', + 'sensor_measurement_12', + 'sensor_measurement_13', + 'sensor_measurement_15', + 'sensor_measurement_17', + 'sensor_measurement_20', + 'sensor_measurement_21'] + feature_columns = [col for col in df.columns if col in required_columns] + if not feature_columns: + raise ValueError(f"No valid feature columns found in the data. Available columns: {df.columns.tolist()}") + + X_test = df[feature_columns].values + logger.info(f"Using {len(feature_columns)} features for prediction: {feature_columns}") + + # Load the StandardScaler + try: + scaler_loaded = joblib.load(scaler_path) + logger.info(f"Successfully loaded scaler from {scaler_path}") + except Exception as e: + raise FileNotFoundError(f"Could not load scaler from {scaler_path}: {e}") + + # Transform the test data using the loaded scaler + X_test_scaled = scaler_loaded.transform(X_test) + + # Load the XGBoost model + try: + with open(model_path, 'rb') as f: + xgb_model = pickle.load(f) + logger.info(f"Successfully loaded XGBoost model from {model_path}") + except Exception as e: + raise FileNotFoundError(f"Could not load XGBoost model from {model_path}: {e}") + + # Make predictions + y_pred = xgb_model.predict(X_test_scaled) + logger.info(f"Generated {len(y_pred)} RUL predictions") + + # Create results DataFrame + results_df = df.copy() + results_df = results_df.rename(columns={'RUL': 'actual_RUL'}) + results_df['predicted_RUL'] = y_pred + + # Save results back to the original JSON file + results_json = results_df.to_dict('records') + with open(data_json_path, 'w') as f: + json.dump(results_json, f, indent=2) + + logger.info(f"Prediction results saved back to original file: {data_json_path}") + + return y_pred, data_json_path + + async def _response_fn(input_message: str) -> str: + """ + Process the input message and generate RUL predictions using trained models. + """ + logger.info(f"Input message: {input_message}") + data_json_path = verify_json_path(input_message) + try: + predictions, output_filepath = predict_rul_from_data( + data_json_path=data_json_path, + scaler_path=config.scaler_path, + model_path=config.model_path, + output_dir=config.output_folder + ) + + # Generate summary statistics + avg_rul = np.mean(predictions) + min_rul = np.min(predictions) + max_rul = np.max(predictions) + std_rul = np.std(predictions) + + # Create response with prediction summary + response = f"""RUL predictions generated successfully! 📊 + +**Prediction Summary:** +- **Total predictions:** {len(predictions)} +- **Average RUL:** {avg_rul:.2f} cycles +- **Minimum RUL:** {min_rul:.2f} cycles +- **Maximum RUL:** {max_rul:.2f} cycles +- **Standard Deviation:** {std_rul:.2f} cycles + +**Results saved to:** {output_filepath} + +The predictions have been added to the original dataset with column name 'predicted_RUL'. The original JSON file has been updated with the RUL predictions. +All columns from the original dataset have been preserved, and the predicted RUL column has been renamed to 'predicted_RUL' and the actual RUL column has been renamed to 'actual_RUL'.""" + + return response + + except FileNotFoundError as e: + error_msg = f"Required file not found for RUL prediction: {e}. Please ensure all model files and data are available." + logger.warn(error_msg) + return error_msg + except ValueError as ve: + error_msg = f"Data validation error for RUL prediction: {ve}. Check the input data format." + logger.warn(error_msg) + return error_msg + except Exception as e: + error_msg = f"Error during RUL prediction: {e}" + logger.warn(error_msg) + return error_msg + + prompt = """ + Predict RUL (Remaining Useful Life) for turbofan engines using trained machine learning models. + + Input: + - Path to a JSON file containing sensor measurements. + + Required columns: + * sensor_measurement_2 + * sensor_measurement_3 + * sensor_measurement_4 + * sensor_measurement_7 + * sensor_measurement_8 + * sensor_measurement_11 + * sensor_measurement_12 + * sensor_measurement_13 + * sensor_measurement_15 + * sensor_measurement_17 + * sensor_measurement_20 + * sensor_measurement_21 + + Process: + 1. Load and preprocess data using StandardScaler + 2. Generate predictions using XGBoost model + 3. Calculate summary statistics (mean, min, max, std dev) + 4. Save predictions to JSON file + + Output: + - RUL predictions for each engine unit + - Summary statistics of predictions + - Updated JSON file with predictions added as 'predicted_RUL' column + """ + yield FunctionInfo.from_fn(_response_fn, + description=prompt) + try: + pass + except GeneratorExit: + logger.info("Predict RUL function exited early!") + finally: + logger.info("Cleaning up predict_rul_tool workflow.") \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/register.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/register.py new file mode 100644 index 000000000..128acf4ee --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/register.py @@ -0,0 +1,16 @@ +# pylint: disable=unused-import +# flake8: noqa + +# Import any tools which need to be automatically registered here +from . import generate_sql_query_and_retrieve_tool +from . import predict_rul_tool +from . import plot_distribution_tool +from . import plot_comparison_tool +from . import plot_line_chart_tool +# from . import plot_sensor_over_RUL_tool +# from . import load_models_and_predict_RUL_tool +# from . import plot_anomaly_tool +# from . import distribution_histogram_rul_tool +# from . import plot_prediction_errors_tool +# from . import plot_rul_error_by_health_state_tool +# from . import generate_chart_tool \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/vanna_util.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/vanna_util.py new file mode 100644 index 000000000..16c6eff1d --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/vanna_util.py @@ -0,0 +1,259 @@ +from vanna.chromadb import ChromaDB_VectorStore +from vanna.base import VannaBase +from langchain_nvidia import ChatNVIDIA +from tqdm import tqdm + +class NIMCustomLLM(VannaBase): + def __init__(self, config=None): + VannaBase.__init__(self, config=config) + + if not config: + raise ValueError("config must be passed") + + # default parameters - can be overrided using config + self.temperature = 0.7 + + if "temperature" in config: + self.temperature = config["temperature"] + + # If only config is passed + if "api_key" not in config: + raise ValueError("config must contain a NIM api_key") + + if "model" not in config: + raise ValueError("config must contain a NIM model") + + api_key = config["api_key"] + model = config["model"] + + # Initialize ChatNVIDIA client + self.client = ChatNVIDIA( + api_key=api_key, + model=model, + temperature=self.temperature, + ) + self.model = model + + def system_message(self, message: str) -> any: + return {"role": "system", "content": message+"\n DO NOT PRODUCE MARKDOWN, ONLY RESPOND IN PLAIN TEXT"} + + def user_message(self, message: str) -> any: + return {"role": "user", "content": message} + + def assistant_message(self, message: str) -> any: + return {"role": "assistant", "content": message} + + def submit_prompt(self, prompt, **kwargs) -> str: + if prompt is None: + raise Exception("Prompt is None") + + if len(prompt) == 0: + raise Exception("Prompt is empty") + + # Count the number of tokens in the message log + # Use 4 as an approximation for the number of characters per token + num_tokens = 0 + for message in prompt: + num_tokens += len(message["content"]) / 4 + print(f"Using model {self.model} for {num_tokens} tokens (approx)") + + response = self.client.invoke(prompt) + return response.content + +class NIMVanna(ChromaDB_VectorStore, NIMCustomLLM): + def __init__(self, VectorConfig = None, LLMConfig = None): + ChromaDB_VectorStore.__init__(self, config=VectorConfig) + NIMCustomLLM.__init__(self, config=LLMConfig) + +class CustomEmbeddingFunction: + """ + A class that can be used as a replacement for chroma's DefaultEmbeddingFunction. + It takes in input (text or list of texts) and returns embeddings using NVIDIA's API. + """ + + def __init__(self, api_key, model="nvidia/nv-embedqa-e5-v5"): + """ + Initialize the embedding function with the API key and model name. + + Parameters: + - api_key (str): The API key for authentication. + - model (str): The model name to use for embeddings (default is "nvidia/nv-embedqa-e5-v5"). + """ + from langchain_nvidia import NVIDIAEmbeddings + + self.embeddings = NVIDIAEmbeddings( + api_key=api_key, + model_name=model, + input_type="query", + truncate="NONE" + ) + + def __call__(self, input): + """ + Call method to make the object callable, as required by chroma's EmbeddingFunction interface. + + Parameters: + - input (str or list): The input data for which embeddings need to be generated. + + Returns: + - embedding (list): The embedding vector(s) for the input data. + """ + # Ensure input is a list, as required by the API + input_data = [input] if isinstance(input, str) else input + + # Generate embeddings + embeddings = [] + for text in input_data: + embedding = self.embeddings.embed_query(text) + embeddings.append(embedding) + + return embeddings[0] if len(embeddings) == 1 and isinstance(input, str) else embeddings + +def initVanna(vn): + # Get and train DDL from sqlite_master + df_ddl = vn.run_sql("SELECT type, sql FROM sqlite_master WHERE sql is not null") + for ddl in df_ddl['sql'].to_list(): + vn.train(ddl=ddl) + + # Define FD datasets and DDL for RUL tables + fd_datasets = ["FD001", "FD002", "FD003", "FD004"] + for fd in fd_datasets: + vn.train(ddl=f""" + CREATE TABLE IF NOT EXISTS RUL_{fd} ( + "unit_number" INTEGER, + "RUL" INTEGER + ) + """) + + # Common sensor columns for train and test tables + sensor_columns = """ + "unit_number" INTEGER, + "time_in_cycles" INTEGER, + "operational_setting_1" REAL, + "operational_setting_2" REAL, + "operational_setting_3" REAL, + "sensor_measurement_1" REAL, + "sensor_measurement_2" REAL, + "sensor_measurement_3" REAL, + "sensor_measurement_4" REAL, + "sensor_measurement_5" REAL, + "sensor_measurement_6" REAL, + "sensor_measurement_7" REAL, + "sensor_measurement_8" REAL, + "sensor_measurement_9" REAL, + "sensor_measurement_10" REAL, + "sensor_measurement_11" REAL, + "sensor_measurement_12" REAL, + "sensor_measurement_13" REAL, + "sensor_measurement_14" REAL, + "sensor_measurement_15" REAL, + "sensor_measurement_16" REAL, + "sensor_measurement_17" INTEGER, + "sensor_measurement_18" INTEGER, + "sensor_measurement_19" REAL, + "sensor_measurement_20" REAL, + "sensor_measurement_21" REAL + """ + + # Create train and test tables for each FD dataset + for fd in fd_datasets: + vn.train(ddl=f"CREATE TABLE IF NOT EXISTS train_{fd} ({sensor_columns})") + vn.train(ddl=f"CREATE TABLE IF NOT EXISTS test_{fd} ({sensor_columns})") + + dataset_documentation = """ + Data sets (FD001, FD002, FD003, FD004) consists of multiple multivariate time series. Each data set is further divided into training and test subsets. + Each time series is from a different engine � i.e., the data can be considered to be from a fleet of engines of the same type. + Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. T + his wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. + These settings are also included in the data. The data is contaminated with sensor noise. + + The engine is operating normally at the start of each time series, and develops a fault at some point during the series. + In the training set, the fault grows in magnitude until system failure. In the test set, the time series ends some time prior to system failure. + The objective is to predict the number of remaining operational cycles before failure in the test set, i.e., the number of operational cycles after the last cycle that the engine will continue to operate. + Also provided a vector of true Remaining Useful Life (RUL) values only for the test data. + + The data are provided as a zip-compressed text file with 26 columns of numbers, separated by spaces. + Each row is a snapshot of data taken during a single operational cycle, each column is a different variable. The columns correspond to: + 1) unit number + 2) time, in cycles + 3) operational setting 1 + 4) operational setting 2 + 5) operational setting 3 + 6) sensor measurement 1 + 7) sensor measurement 2 + ... + 26) sensor measurement 26 + """ + + # Additional training examples + vn.train(documentation=dataset_documentation) + + # Additional training examples + # vn.train(documentation="The engine_sensor_data table tracks operational settings and sensor measurements for multiple engines in a fleet. It includes Remaining Useful Life (RUL) predictions for each engine.") + + queries = [ + "SELECT * FROM train_FD001 AS t JOIN RUL_FD001 AS r ON t.unit_number = r.unit_number ORDER BY t.time_in_cycles DESC LIMIT 10", + "SELECT unit_number, AVG(sensor_measurement_1), AVG(sensor_measurement_2), AVG(sensor_measurement_3) FROM train_FD001 GROUP BY unit_number", + "SELECT unit_number, SUM(sensor_measurement_1), SUM(sensor_measurement_2), SUM(sensor_measurement_3) FROM train_FD001 GROUP BY unit_number", + "SELECT * FROM train_FD002 WHERE time_in_cycles BETWEEN 50 AND 100", + "SELECT * FROM train_FD003 WHERE unit_number = 1 ORDER BY time_in_cycles ASC", + """ + SELECT unit_number, + MAX(sensor_measurement_1) AS max_sensor1, + MIN(sensor_measurement_1) AS min_sensor1, + AVG(sensor_measurement_1) AS avg_sensor1 + FROM train_FD004 + GROUP BY unit_number + """, + """ + SELECT unit_number, + AVG(sensor_measurement_5) AS avg_sensor5, + AVG(sensor_measurement_10) AS avg_sensor10 + FROM train_FD001 + GROUP BY unit_number + """, + "SELECT unit_number, MAX(time_in_cycles) AS last_cycle FROM train_FD002 GROUP BY unit_number", + "SELECT * FROM train_FD003 WHERE sensor_measurement_17 < 0 OR sensor_measurement_18 < 0", + """ + SELECT unit_number, + STDDEV(sensor_measurement_1) AS std_sensor1, + STDDEV(sensor_measurement_2) AS std_sensor2, + STDDEV(sensor_measurement_3) AS std_sensor3 + FROM train_FD001 + WHERE unit_number = 2 + GROUP BY unit_number + """, + """ + SELECT unit_number, + SUM(sensor_measurement_1) AS sum_sensor1, + SUM(sensor_measurement_2) AS sum_sensor2, + SUM(sensor_measurement_3) AS sum_sensor3, + SUM(sensor_measurement_4) AS sum_sensor4, + SUM(sensor_measurement_5) AS sum_sensor5 + FROM train_FD004 + GROUP BY unit_number + """, + "SELECT * FROM test_FD002 WHERE sensor_measurement_2 > 100", + """ + SELECT unit_number, + MAX(sensor_measurement_3) AS max_sensor3, + MIN(sensor_measurement_6) AS min_sensor6, + AVG(sensor_measurement_9) AS avg_sensor9 + FROM test_FD003 + GROUP BY unit_number + """, + "SELECT * FROM test_FD004 WHERE unit_number IN (1, 2, 3) ORDER BY time_in_cycles ASC", + ] + + for query in tqdm(queries, desc="Training NIMVanna"): + vn.train(sql=query) + + # Additional specific training cases + vn.train(question="Retrieve the time_in_cycles and operational_setting_1 from the test_FD001 for all records where the unit_id is equal to 1.", + sql="SELECT time_in_cycles, operational_setting_1 FROM test_FD001 WHERE unit_number = 1") + vn.train(question="Retrieve the time_in_cycles and sensor_measurement_1 from the test_FD001 for all records where the unit_id is equal to 1.", + sql="SELECT time_in_cycles, sensor_measurement_1 FROM test_FD001 WHERE unit_number = 1") + vn.train(question="Retrieve RUL of each unit from the train_FD001", + sql="SELECT unit_number, MAX(time_in_cycles) AS RUL FROM train_FD001 GROUP BY unit_number") + vn.train(question="Retrieve the unit_number, time_in_cycles, real time Remaining Useful Life (RUL), and sensor_measurement_3 from table train_FD001, ordered by unit_number and time_in_cycles.", sql="SELECT unit_number, time_in_cycles, MAX(time_in_cycles) OVER (PARTITION BY unit_number) - time_in_cycles AS RUL, sensor_measurement_3 FROM train_FD001 ORDER BY unit_number, time_in_cycles") + From 851e22c8d3b52c204d7bb70660c60ebf4c453fe3 Mon Sep 17 00:00:00 2001 From: Vineeth Kalluru Date: Fri, 6 Jun 2025 14:28:39 -0500 Subject: [PATCH 04/11] Simpler Readme Signed-off-by: Vineeth Kalluru --- .gitignore | 5 +- .../predictive_maintenance_agent/README.md | 659 ++---------------- 2 files changed, 70 insertions(+), 594 deletions(-) diff --git a/.gitignore b/.gitignore index deaecfc32..3cd415db0 100644 --- a/.gitignore +++ b/.gitignore @@ -34,4 +34,7 @@ RAG/notebooks/langchain/data/save_embedding .idea # Environment variables -.env \ No newline at end of file +.env + +# egg-info directories +**/egg-info \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/README.md b/industries/manufacturing/predictive_maintenance_agent/README.md index 1d6552742..34be58116 100644 --- a/industries/manufacturing/predictive_maintenance_agent/README.md +++ b/industries/manufacturing/predictive_maintenance_agent/README.md @@ -4,690 +4,163 @@ A comprehensive AI-powered predictive maintenance system built with NVIDIA AIQ T Work done by: Vineeth Kalluru, Janaki Vamaraju, Sugandha Sharma, Ze Yang and Viraj Modak -## Table of Contents -- [Need for Predictive Maintenance](#need-for-predictive-maintenance) -- [NASA Turbofan Engine Dataset](#nasa-turbofan-engine-dataset) -- [Architecture](#architecture) -- [Setup and Installation](#setup-and-installation) -- [Launching AIQ Server and Frontend](#launching-aiq-server-and-frontend) -- [Testing with Example Prompts](#testing-with-example-prompts) -- [Appendix: Phoenix Observability](#appendix-phoenix-observability) -- [Next Steps](#next-steps) +## Overview -## Need for Predictive Maintenance +Predictive maintenance prevents costly downtime by identifying potential failures before they occur. This agent leverages AI to analyze sensor data from turbofan engines, predict remaining useful life (RUL), and provide actionable insights for maintenance teams. -In today's industrial landscape, unplanned equipment failures can cost organizations millions of dollars in downtime, emergency repairs, and lost productivity. Traditional reactive maintenance approaches wait for equipment to fail before taking action, while scheduled preventive maintenance often leads to unnecessary interventions and costs. +### Key Benefits +- **Prevent Costly Downtime**: Identify failures before they occur +- **Optimize Maintenance**: Perform maintenance only when needed +- **Extend Equipment Life**: Monitor health to maximize efficiency +- **Improve Safety**: Prevent catastrophic failures +- **Reduce Costs**: Minimize emergency repairs and disruptions -**Predictive maintenance** revolutionizes this approach by: +## Dataset -- **Preventing Costly Downtime**: Identifying potential failures before they occur, allowing for planned maintenance windows -- **Optimizing Maintenance Schedules**: Performing maintenance only when needed, reducing unnecessary interventions -- **Extending Equipment Lifespan**: Monitoring equipment health to maximize operational efficiency -- **Improving Safety**: Preventing catastrophic failures that could endanger personnel -- **Reducing Costs**: Minimizing emergency repairs, spare parts inventory, and operational disruptions - -This predictive maintenance agent leverages AI and machine learning to analyze sensor data from turbofan engines, predict remaining useful life (RUL), and provide actionable insights for maintenance teams. - -## NASA Turbofan Engine Dataset - -The system utilizes the **NASA Turbofan Engine Degradation Simulation Dataset (C-MAPSS)**, a widely-used benchmark dataset for prognostics and health management research in manufacturing domain. - -### Dataset Overview - -The NASA dataset contains simulated turbofan engine run-to-failure data with the following characteristics: - -- **21 Sensor Measurements**: Including temperature, pressure, vibration, and flow measurements -- **3 Operational Settings**: Different flight conditions and altitudes +Uses the **NASA Turbofan Engine Degradation Simulation Dataset (C-MAPSS)** with: +- **21 Sensor Measurements**: Temperature, pressure, vibration, and flow +- **3 Operational Settings**: Different flight conditions - **Multiple Engine Units**: Each with unique degradation patterns - **Run-to-Failure Data**: Complete lifecycle from healthy operation to failure -### Key Sensor Parameters - -| Sensor ID | Description | Unit | -|-----------|-------------|------| -| sensor_1 | Total temperature at fan inlet | °R | -| sensor_2 | Total temperature at LPC outlet | °R | -| sensor_3 | Total temperature at HPC outlet | °R | -| sensor_4 | Total temperature at LPT outlet | °R | -| sensor_11 | Static pressure at HPC outlet | psia | -| sensor_13 | Corrected fan speed | rpm | -| sensor_20 | Ratio of fuel flow to Ps30 | pps/psi | -| sensor_21 | Corrected fan speed | rpm | - -### Dataset Structure - -``` -Training Data: Engine run-to-failure trajectories -Test Data: Engine trajectories of unknown remaining cycles -Ground Truth: Actual RUL values for test engines -``` - -The dataset enables the development of prognostic models that can predict when an engine will require maintenance based on its current sensor readings and historical degradation patterns. - ## Architecture -The Predictive Maintenance Agent follows a modular, multi-agent architecture built on the NVIDIA AIQ Toolkit framework: - -``` - -``` - -### Key Components - -#### 1. **React Agent Workflow** -- Main orchestration layer using ReAct (Reasoning + Acting) pattern -- Coordinates between different tools and agents -- Handles user queries and tool selection - -#### 2. **SQL Retriever Tool** -- Generates SQL queries using NIM LLM -- Retrieves sensor data from SQLite database -- Uses vector embeddings for semantic query understanding - -#### 3. **RUL Prediction Tool** -- Predicts Remaining Useful Life using pre-trained XGBoost model -- Processes sensor data through feature engineering pipeline -- Provides uncertainty estimates and confidence intervals - -#### 4. **Plotting Agent** -- Multi-tool agent for data visualization -- Generates interactive charts and plots -- Supports line charts, distributions, and comparisons - -#### 5. **Vector Database** -- ChromaDB for storing and retrieving database schema information -- Enables semantic search over table structures and relationships +Multi-agent architecture with: +- **React Agent Workflow**: Main orchestration using ReAct pattern +- **SQL Retriever Tool**: Generates SQL queries using NIM LLM +- **RUL Prediction Tool**: XGBoost model for remaining useful life prediction +- **Plotting Agent**: Multi-tool agent for data visualization +- **Vector Database**: ChromaDB for schema information storage ## Setup and Installation ### Prerequisites - - Python 3.11+ (< 3.13) - Conda or Miniconda - NVIDIA NIM API access -- Node.js v18+ (for AgentIQ web interface) -- Docker (for Phoenix observability - optional) - -### 1. Create a New Conda Environment +- Node.js v18+ (for web interface) -Create and activate a dedicated conda environment for the predictive maintenance agent: +### 1. Create Conda Environment ```bash -# Create new conda environment conda create -n pdm python=3.11 - -# Activate the environment conda activate pdm ``` -### 2. Install NVIDIA AgentIQ from Source - -Clone and install the NVIDIA AIQToolkit following the [official installation guide](https://docs.nvidia.com/agentiq/latest/intro/install.html#install-from-source): +### 2. Install NVIDIA AIQ Toolkit ```bash -# Clone the AIQToolkit repository git clone https://github.com/NVIDIA/AIQToolkit.git cd AIQToolkit - -# Install AIQ from source uv pip install -e . - -# Verify installation aiq --help -# Optional: Remove the cloned repository after installation -# You can safely delete the AIQToolkit directory once installation is complete +# Optional: Remove cloned repo after installation # cd .. && rm -rf AIQToolkit ``` -### 3. Install the Predictive Maintenance Workflow - -Now install the predictive maintenance agent as a custom workflow +### 3. Install Predictive Maintenance Agent ```bash -# Navigate back to your working directory cd .. - -# Clone the Generative AI examples repository git clone https://github.com/NVIDIA/GenerativeAIExamples.git cd GenerativeAIExamples/industries/manufacturing/predictive_maintenance_agent - -# Install the workflow and its dependencies uv pip install -e . ``` -This will install the predictive maintenance agent as a custom AgentIQ workflow with all required dependencies. - ### 4. Environment Setup -Create a `.env` file in the predictive maintenance agent directory with your NVIDIA NIM credentials: - +Create `.env` file: ```bash -# .env file NVIDIA_API_KEY=your_nvidia_api_key_here NIM_BASE_URL=https://integrate.api.nvidia.com/v1 ``` -**Important**: Make sure to replace `your_nvidia_api_key_here` with your actual NVIDIA API key. - -The environment variables need to be loaded before starting the AIQ server. You have several options: - -**Source the environment manually** +Load environment variables: ```bash -# Load environment variables manually (Linux/Mac) export $(cat .env | grep -v '^#' | xargs) - -# For Windows Command Prompt -for /f "tokens=*" %i in (.env) do set %i - -# For Windows PowerShell -Get-Content .env | ForEach-Object { if ($_ -match "^([^#].*)=(.*)") { [Environment]::SetEnvironmentVariable($matches[1], $matches[2]) } } ``` ### 5. Database Setup -The predictive maintenance agent requires the NASA turbofan dataset to be loaded into a SQLite database. We provide a comprehensive setup script to convert the original NASA text files into a structured database. - -#### Download the NASA Dataset - -First, download the NASA Turbofan Engine Degradation Simulation Dataset (C-MAPSS): - -1. Visit the [NASA Prognostics Data Repository](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/) -2. Download the "Turbofan Engine Degradation Simulation Data Set" -3. Extract all text files to a `data/` directory in your project - -The dataset should include these files: -``` -data/ -├── train_FD001.txt # Training data - Sea level, single fault mode -├── train_FD002.txt # Training data - Sea level, multiple fault modes -├── train_FD003.txt # Training data - High altitude, single fault mode -├── train_FD004.txt # Training data - High altitude, multiple fault modes -├── test_FD001.txt # Test data - Sea level, single fault mode -├── test_FD002.txt # Test data - Sea level, multiple fault modes -├── test_FD003.txt # Test data - High altitude, single fault mode -├── test_FD004.txt # Test data - High altitude, multiple fault modes -├── RUL_FD001.txt # Ground truth RUL values for test data -├── RUL_FD002.txt # Ground truth RUL values for test data -├── RUL_FD003.txt # Ground truth RUL values for test data -└── RUL_FD004.txt # Ground truth RUL values for test data -``` - -#### Run the Database Setup Script - -Convert the NASA text files to SQLite database: - +1. Download [NASA Turbofan Dataset](https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/) +2. Extract files to `data/` directory +3. Run setup script: ```bash -# Basic usage (assumes data/ directory exists) python setup_database.py - -# Custom data directory and database path -python setup_database.py --data-dir /path/to/nasa/files --db-path /path/to/output.db - -# Get help on available options -python setup_database.py --help -``` - -The script will create a comprehensive SQLite database with the following structure: - -#### Database Tables Created - -| Table Name | Description | Records | -|------------|-------------|---------| -| `training_data` | Complete engine run-to-failure trajectories | ~20,000+ | -| `test_data` | Engine test trajectories (unknown RUL) | ~13,000+ | -| `rul_data` | Ground truth RUL values for test engines | ~400+ | -| `sensor_metadata` | Descriptions of all 21 sensors | 21 | -| `dataset_metadata` | Information about the 4 sub-datasets | 4 | - -#### Database Views Created - -| View Name | Purpose | -|-----------|---------| -| `latest_sensor_readings` | Most recent sensor readings per engine | -| `engine_health_summary` | Aggregated health statistics per engine | - -#### Sample Database Queries - -Once the database is set up, you can run sample queries: - -```sql --- View engine health summary -SELECT * FROM engine_health_summary LIMIT 5; - --- Get sensor readings for a specific engine -SELECT unit_number, time_in_cycles, sensor_measurement_1, sensor_measurement_11, RUL -FROM training_data -WHERE unit_number = 1 AND dataset = 'FD001' -ORDER BY time_in_cycles; - --- Find engines with shortest operational life -SELECT unit_number, dataset, MAX(time_in_cycles) as total_cycles -FROM training_data -GROUP BY unit_number, dataset -ORDER BY total_cycles -LIMIT 10; -``` - -#### Verification - -The setup script automatically validates the database creation. You should see output similar to: - -``` -✅ Database created successfully at: PredM_db/nasa_turbo.db - -Database contains the following tables: -- training_data: Engine run-to-failure trajectories -- test_data: Engine test trajectories -- rul_data: Ground truth RUL values -- sensor_metadata: Sensor descriptions -- dataset_metadata: Dataset information - -Useful views created: -- latest_sensor_readings: Latest readings per engine -- engine_health_summary: Engine health statistics ``` ### 6. Configure Paths -Update the paths in `configs/config.yml` to match your local environment: - -```yaml -functions: - sql_retriever: - vector_store_path: "/path/to/your/PredM_db" - db_path: "/path/to/your/PredM_db/nasa_turbo.db" - output_folder: "/path/to/your/output_data" - predict_rul: - scaler_path: "/path/to/your/models/scaler_model.pkl" - model_path: "/path/to/your/models/xgb_model_fd001.pkl" - output_folder: "/path/to/your/output_data" -``` - -### 7. Verify Installation +Update `configs/config.yml` with your local paths for database, models, and output directories. -Verify that both AIQ and the workflow are properly installed: +## Launch Server and UI +### Start AIQ Server ```bash -pip list | grep -E "aiq|predictive" +aiq serve --config_file=configs/config.yml ``` +Server runs on `http://localhost:8000` -## Launching AIQ Server and Frontend - -### 1. Start the AIQ Server +### Setup Web Interface -**Important**: Ensure environment variables are loaded before starting the server. +```bash +git clone https://github.com/NVIDIA/AIQToolkit-UI.git +cd AIQToolkit-UI +npm ci +npm run dev +``` +UI available at `http://localhost:3000` -#### Load environment variables +**Configure UI Settings:** +- Click Settings icon (bottom left) +- Set HTTP URL to `/chat/stream` (recommended) +- Configure theme and WebSocket URL as needed -If you prefer to load environment variables manually: +## Example Prompts -```bash -# Linux/Mac: Load environment variables and start server -export $(cat .env | grep -v '^#' | xargs) && aiq serve --config_file=configs/config.yml +Test the system with these prompts: -# Alternative: Source variables first, then start server -source <(cat .env | grep -v '^#' | sed 's/^/export /') -aiq serve --config_file=configs/config.yml +**Data Retrieval:** +``` +Show me sensor readings for engine unit 1 for the last 50 cycles. +Retrieve RUL of each unit from train_FD001 table and plot the distribution. ``` -The server will start on `http://localhost:8000` by default and you should see output similar to: - +**Visualization:** ``` -2025-03-07 12:54:20,394 - aiq.cli.commands.start - INFO - Starting AgentIQ from config file: 'configs/config.yml' -INFO: Started server process [47250] -INFO: Waiting for application startup. -INFO: Application startup complete. -INFO: Uvicorn running on http://localhost:8000 (Press CTRL+C to quit) +Plot degradation trend of sensor_measurement_11 vs time_in_cycles for engine unit 2. +Create comparison plot showing sensor_measurement_21 for engines 1 and 2. ``` -**Note**: If you see any errors related to missing API keys, verify that your `.env` file is properly formatted and that environment variables are loaded correctly. - -### 2. Launch the AgentIQ Web User Interface - -AgentIQ provides a dedicated web interface that requires Node.js v18+. - -#### Setup the AIQtoolkit UI - -The NVIDIA AIQToolkit UI provides a modern web interface for interacting with AIQ workflows. Follow these steps to set up the UI: - -**Prerequisites:** -- Node.js v18+ -- Git - -**Installation:** - -1. **Clone the AIQToolkit-UI repository:** - ```bash - git clone https://github.com/NVIDIA/AIQToolkit-UI.git - cd AIQToolkit-UI - ``` - -2. **Install dependencies:** - ```bash - npm ci - ``` - -3. **Run the development server:** - ```bash - npm run dev - ``` - - The application will be available at `http://localhost:3000` - -**Alternative: Docker Deployment** - -For production or containerized environments: - -```bash -# Build the Docker image -docker build -t aiqtoolkit-ui . - -# Run the container -docker run -p 3000:3000 aiqtoolkit-ui +**Prediction:** ``` - -#### Configure the UI Settings - -Once the UI is running: - -1. Open the web interface in your browser at `http://localhost:3000` -2. Click the **Settings** icon in the bottom left corner -3. Configure the connection settings: - - **HTTP URL for Chat Completion**: Choose your preferred endpoint: - - `/generate` - Non-streaming responses - - `/generate/stream` - Streaming responses (recommended) - - `/chat` - Chat-based non-streaming - - `/chat/stream` - Chat-based streaming (recommended for intermediate results) - - **Theme**: Select Light or Dark theme - - **WebSocket URL**: For real-time connections (if needed) - -**Note**: The UI supports both HTTP requests (OpenAI compatible) and WebSocket connections for server communication. - -### 3. Alternative: Direct API Interaction - -You can also interact with the server directly via REST API: - -```bash -# Non-streaming request -curl -X POST http://localhost:8000/generate \ - -H "Content-Type: application/json" \ - -d '{"input_message": "What is the current health status of engine unit 1?", "use_knowledge_base": true}' - -# Chat-based request -curl -X POST http://localhost:8000/chat \ - -H "Content-Type: application/json" \ - -d '{"message": "What is the current health status of engine unit 1?"}' +Predict remaining useful life for engine unit 5 based on current sensor readings. +Retrieve data for engine unit 24, predict RUL, and compare actual vs predicted values. ``` -### 4. Verify Installation - -Once both server and frontend are running: - -1. Navigate to the web interface (`http://localhost:3000`) -2. Configure the settings to point to your server -3. Test with a simple question like: "Show me sensor data for engine unit 1" -4. Verify you receive both intermediate steps and final results - -## Testing with Example Prompts - -Here are comprehensive example prompts to test different capabilities of the predictive maintenance agent: - -### Data Retrieval and Analysis - -* **Basic Data Query** - ``` - Show me the sensor readings for engine unit 1 for the last 50 cycles. - ``` - -* **Sensor Analysis** - ``` - What are the temperature sensor readings (sensors 1-4) for engine units 1-5 over their operational lifetime? - ``` - -* **Operational Settings Analysis** - ``` - Compare the operational settings across different engine units and show me which units operated under the most severe conditions. - ``` +## Observability (Optional) -* **Operational Settings Data Retrieval** - ``` - Retrieve the time_in_cycles and operational_setting_1 from the test_FD001 table for unit number 1. Then plot operational_setting_1 over time_in_cycles. - ``` - -* **RUL Data Analysis** - ``` - Retrieve RUL of each unit from the train_FD001 table. Then plot the distribution of RUL. - ``` - -### Data Visualization - -* **Trend Analysis** - ``` - Plot the degradation trend of sensor_measurement_11 vs time_in_cycles for engine unit 2. - ``` - -* **Multi-Engine Comparison** - ``` - Create a comparison plot showing sensor_measurement_21 for engines 1 and 2 to identify different degradation patterns. - ``` - -* **Distribution Analysis** - ``` - Show me the distribution of sensor_measurement_4 values across all engine units to identify outliers. - ``` - -### Predictive Analytics - -* **RUL Prediction** - ``` - Predict the remaining useful life for engine unit 5 based on its current sensor readings. - ``` - -### Advanced Queries - -* **Comprehensive RUL Analysis with Comparison** - ``` - Retrieve time in cycles, all sensor measurements and RUL value for engine unit 24 from FD001 test and RUL tables. Predict RUL for it. Finally, generate a plot to compare actual RUL value with predicted RUL value across time. - ``` - -* **Anomaly Detection** - ``` - Identify any anomalous sensor readings in the last 100 cycles for all active engines. - ``` - -## Appendix: Phoenix Observability - -[Phoenix](https://phoenix.arize.com/) provides comprehensive observability and monitoring for your AI applications, enabling you to track performance, debug issues, and optimize your predictive maintenance system. - -### Starting Phoenix - -#### Option 1: Docker (Recommended) +Monitor your system with Phoenix: ```bash -# Start Phoenix server +# Docker (recommended) docker run -p 6006:6006 -p 4317:4317 arizephoenix/phoenix:latest -# Phoenix will be available at http://localhost:6006 -``` - -#### Option 2: Python Package - -```bash -# Install Phoenix +# Or install as package uv pip install arize-phoenix - -# Start Phoenix server phoenix serve ``` -### Configuration - -Phoenix is pre-configured in `configs/config.yml`: - -```yaml -general: - tracing: - phoenix: - _type: phoenix - endpoint: http://localhost:6006/v1/traces - project: predictive-maintenance-app -``` - -### Monitoring Capabilities - -Once Phoenix is running and your agents are active, you can monitor: - -1. **Trace Analysis**: View detailed execution traces of agent workflows -2. **LLM Interactions**: Monitor all LLM calls, prompts, and responses -3. **Tool Usage**: Track which tools are being used and their performance -4. **Error Detection**: Identify and debug failures in the agent pipeline -5. **Performance Metrics**: Analyze response times and system performance -6. **Cost Tracking**: Monitor API usage and associated costs - -### Accessing Phoenix Dashboard - -1. Navigate to `http://localhost:6006` -2. Select the `predictive-maintenance-app` project -3. Explore traces, sessions, and performance metrics -4. Use filters to focus on specific time periods or components - -### Best Practices - -- **Regular Monitoring**: Check Phoenix dashboard regularly for performance insights -- **Error Analysis**: Use trace data to quickly identify and resolve issues -- **Optimization**: Identify slow components and optimize based on performance data -- **Cost Management**: Monitor API usage to optimize costs +Access dashboard at `http://localhost:6006` to monitor traces, performance, and costs. ## Next Steps -The predictive maintenance agent provides a solid foundation for industrial AI applications. Here are planned enhancements to further improve its capabilities: - -### 1. Adding Memory Layer - -**Objective**: Enable the agent to remember previous interactions and build context over time. - -**Implementation Plan**: -- Integrate conversation memory using LangChain's memory components -- Store user preferences and historical analysis patterns -- Implement semantic memory for retaining domain knowledge -- Add session persistence for multi-turn conversations - -**Benefits**: -- Improved user experience with contextual responses -- Better understanding of recurring maintenance patterns -- Personalized recommendations based on user history - -### 2. Parallel Tool Calls for Data Visualization - -**Objective**: Enhance performance by executing multiple visualization tasks simultaneously. - -**Implementation Plan**: -- Implement async tool execution framework -- Create parallel plotting workflows for multiple engine units -- Add batch processing for large-scale data analysis -- Optimize resource utilization for concurrent operations - -**Benefits**: -- Significantly faster response times for complex queries -- Improved system scalability -- Enhanced user experience with rapid visualizations - -### 3. Action Recommendation Reasoning Agent - -**Objective**: Develop an intelligent agent that provides specific maintenance recommendations. - -**Implementation Plan**: -- Create a specialized reasoning agent using advanced prompt engineering -- Integrate maintenance best practices and industry standards -- Implement cost-benefit analysis for maintenance decisions -- Add integration with maintenance management systems - -**Capabilities**: -- Prioritized maintenance recommendations -- Risk assessment and mitigation strategies -- Resource allocation optimization -- Compliance with safety regulations - -### 4. Fault Detection Agent - -**Objective**: Implement real-time fault detection and classification capabilities. - -**Implementation Plan**: -- Develop anomaly detection algorithms using statistical methods -- Implement pattern recognition for fault signatures -- Create alert systems for critical failures -- Add fault classification and root cause analysis - -**Features**: -- Real-time monitoring and alerting -- Automated fault classification -- Historical fault pattern analysis -- Integration with existing monitoring systems - -### 5. NV-Tesseract Foundation Model Integration - -**Objective**: Replace existing ML models with NVIDIA's state-of-the-art [NV-Tesseract time series foundation models](https://developer.nvidia.com/blog/new-nvidia-nv-tesseract-time-series-models-advance-dataset-processing-and-anomaly-detection/). - -#### About NV-Tesseract - -NV-Tesseract represents a breakthrough in time-series AI, offering specialized models for different predictive tasks: - -- **Advanced Architecture**: Transformer-based embeddings with multi-head attention layers -- **Modular Design**: Purpose-built models for anomaly detection, forecasting, and classification -- **Superior Performance**: 5-20% accuracy improvements over traditional methods -- **Industry Applications**: Proven success in manufacturing, finance, healthcare, and industrial processes - -#### Integration Plan - -**Phase 1: Model Evaluation** -- Benchmark NV-Tesseract against current XGBoost models -- Evaluate performance on NASA turbofan dataset -- Compare accuracy, precision, and recall metrics - -**Phase 2: Architecture Migration** -- Replace existing prediction pipeline with NV-Tesseract models -- Implement transformer-based feature processing -- Optimize inference performance for real-time predictions - -**Phase 3: Enhanced Capabilities** -- **Anomaly Detection**: Implement real-time operational anomaly detection with F1 scores up to 0.96 -- **Advanced Forecasting**: Add multi-horizon forecasting for maintenance planning -- **Classification**: Enhance fault type classification with improved accuracy - -**Expected Benefits**: -- **Improved Accuracy**: Superior prediction performance, especially on complex multivariate datasets -- **Better Generalization**: Enhanced ability to handle unfamiliar data patterns -- **Real-time Processing**: Optimized inference for production environments -- **Reduced False Positives**: More precise anomaly detection reducing unnecessary maintenance - -#### Implementation Timeline - -1. **Q2 2025**: NV-Tesseract model evaluation and integration planning -2. **Q3 2025**: Core model replacement and testing -3. **Q4 2025**: Production deployment and performance optimization - -### Getting Started with Next Steps - -To contribute to these enhancements: - -1. **Fork the Repository**: Create your own branch for development -2. **Review Current Architecture**: Understand the existing codebase structure -3. **Choose Enhancement**: Select a specific improvement area to focus on -4. **Development Plan**: Create detailed implementation plan -5. **Testing Strategy**: Develop comprehensive testing approach -6. **Documentation**: Update documentation and examples - -For more information about contributing or to discuss specific enhancements, please reach out to the development team. +The agent provides a foundation for industrial AI applications. Planned enhancements include: memory layer for context retention, parallel tool execution for faster responses, action recommendation reasoning agent, real-time fault detection, and integration with NVIDIA's NV-Tesseract foundation models for improved accuracy. --- -**Contact Information**: -- Author: Vineeth Kalluru -- Maintainer: NVIDIA Corporation -- Framework: NVIDIA AIQ Toolkit - -**Additional Resources**: +**Resources:** - [NVIDIA AIQ Toolkit Documentation](https://docs.nvidia.com/aiq-toolkit/) -- [NV-Tesseract Model Information](https://developer.nvidia.com/blog/new-nvidia-nv-tesseract-time-series-models-advance-dataset-processing-and-anomaly-detection/) -- [Phoenix Observability Platform](https://phoenix.arize.com/) \ No newline at end of file +- [Phoenix Observability](https://phoenix.arize.com/) +- [NV-Tesseract Models](https://developer.nvidia.com/blog/new-nvidia-nv-tesseract-time-series-models-advance-dataset-processing-and-anomaly-detection/) \ No newline at end of file From 822c7df76977b4dc2d952f1edb51985a446e33d4 Mon Sep 17 00:00:00 2001 From: Vineeth Kalluru Date: Fri, 6 Jun 2025 14:32:05 -0500 Subject: [PATCH 05/11] test prompts added Signed-off-by: Vineeth Kalluru --- .../predictive_maintenance_agent/README.md | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/industries/manufacturing/predictive_maintenance_agent/README.md b/industries/manufacturing/predictive_maintenance_agent/README.md index 34be58116..6eb822a09 100644 --- a/industries/manufacturing/predictive_maintenance_agent/README.md +++ b/industries/manufacturing/predictive_maintenance_agent/README.md @@ -123,20 +123,15 @@ Test the system with these prompts: **Data Retrieval:** ``` -Show me sensor readings for engine unit 1 for the last 50 cycles. -Retrieve RUL of each unit from train_FD001 table and plot the distribution. +Retrieve RUL of each unit from the FD001 dataset. Then plot the distribution of RUL. ``` - **Visualization:** ``` -Plot degradation trend of sensor_measurement_11 vs time_in_cycles for engine unit 2. -Create comparison plot showing sensor_measurement_21 for engines 1 and 2. +Retrieve the time in cycles and operational setting 1 from the FD001 test table for unit number 1 and plot its value vs time. ``` - -**Prediction:** +**Prediction** ``` -Predict remaining useful life for engine unit 5 based on current sensor readings. -Retrieve data for engine unit 24, predict RUL, and compare actual vs predicted values. +Retrieve time in cycles, all sensor measurements and RUL value for engine unit 24 from FD001 test and RUL tables. Predict RUL for it. Finally, generate a plot to compare actual RUL value with predicted RUL value across time. ``` ## Observability (Optional) From 676339f9b1aecc1db613ece087f82aad83c309e0 Mon Sep 17 00:00:00 2001 From: Vineeth Kalluru Date: Fri, 6 Jun 2025 17:18:31 -0500 Subject: [PATCH 06/11] Verified full flow. Added screenshots Signed-off-by: Vineeth Kalluru --- .../predictive_maintenance_agent/README.md | 28 +++++++++++++------ .../configs/config.yml | 20 ++++++------- 2 files changed, 30 insertions(+), 18 deletions(-) diff --git a/industries/manufacturing/predictive_maintenance_agent/README.md b/industries/manufacturing/predictive_maintenance_agent/README.md index 6eb822a09..9b4f1f964 100644 --- a/industries/manufacturing/predictive_maintenance_agent/README.md +++ b/industries/manufacturing/predictive_maintenance_agent/README.md @@ -32,6 +32,8 @@ Multi-agent architecture with: - **Plotting Agent**: Multi-tool agent for data visualization - **Vector Database**: ChromaDB for schema information storage +![Architecture Workflow](imgs/intermediate_steps.png) + ## Setup and Installation ### Prerequisites @@ -70,15 +72,8 @@ uv pip install -e . ### 4. Environment Setup -Create `.env` file: -```bash -NVIDIA_API_KEY=your_nvidia_api_key_here -NIM_BASE_URL=https://integrate.api.nvidia.com/v1 -``` - -Load environment variables: ```bash -export $(cat .env | grep -v '^#' | xargs) +export NVIDIA_API_KEY=your_nvidia_api_key_here ``` ### 5. Database Setup @@ -102,6 +97,15 @@ aiq serve --config_file=configs/config.yml ``` Server runs on `http://localhost:8000` +Note: When using the provided config file, you need to set the PWD_PATH environment variable before starting the AIQ server. This ensures the server can locate all required paths correctly. + +Here's how to do it: + +```bash +export PWD_PATH=$(pwd) +aiq serve --config_file=configs/config.yml "$@" +``` + ### Setup Web Interface ```bash @@ -125,15 +129,23 @@ Test the system with these prompts: ``` Retrieve RUL of each unit from the FD001 dataset. Then plot the distribution of RUL. ``` + +![Data Retrieval Example](imgs/test_prompt_1.png) + **Visualization:** ``` Retrieve the time in cycles and operational setting 1 from the FD001 test table for unit number 1 and plot its value vs time. ``` + +![Visualization Example](imgs/test_prompt_2.png) + **Prediction** ``` Retrieve time in cycles, all sensor measurements and RUL value for engine unit 24 from FD001 test and RUL tables. Predict RUL for it. Finally, generate a plot to compare actual RUL value with predicted RUL value across time. ``` +![Prediction Example](imgs/test_prompt_3.png) + ## Observability (Optional) Monitor your system with Phoenix: diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml index 8f12f674f..6f42fc3d7 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml @@ -35,23 +35,23 @@ functions: _type: generate_sql_query_and_retrieve_tool llm_name: sql_llm embedding_name: vanna_embedder - vector_store_path: "./database" - db_path: "./database/nasa_turbo.db" - output_folder: "./output_data" + vector_store_path: "${PWD_PATH}/database" + db_path: "${PWD_PATH}/database/nasa_turbo.db" + output_folder: "${PWD_PATH}/output_data" predict_rul: _type: predict_rul_tool - output_folder: "./output_data" - scaler_path: "./models/scaler_model.pkl" - model_path: "./models/xgb_model_fd001.pkl" + output_folder: "${PWD_PATH}/output_data" + scaler_path: "${PWD_PATH}/models/scaler_model.pkl" + model_path: "${PWD_PATH}/models/xgb_model_fd001.pkl" plot_distribution: _type: plot_distribution_tool - output_folder: "./output_data" + output_folder: "${PWD_PATH}/output_data" plot_comparison: _type: plot_comparison_tool - output_folder: "./output_data" + output_folder: "${PWD_PATH}/output_data" plot_line_chart: _type: plot_line_chart_tool - output_folder: "./output_data" + output_folder: "${PWD_PATH}/output_data" plotting_agent: _type: react_agent llm_name: nim_llm @@ -60,7 +60,7 @@ functions: handle_tool_errors: true max_iterations: 1 description: "Use this agent to generate plots from the retrieved SQL data. Provide a description of the plot required along with the JSON file path containing the SQL output in the input message. - for example: Plot the sensor_measurement_1 vs time_in_cycles for unit 1 from the file ./output_data/sql_output.json" + for example: Plot the sensor_measurement_1 vs time_in_cycles for unit 1 from the file ${PWD_PATH}/output_data/sql_output.json" workflow: _type: react_agent From d220921a55ca25f68300750c20975ddf1a94e913 Mon Sep 17 00:00:00 2001 From: Vineeth Kalluru Date: Fri, 6 Jun 2025 17:21:45 -0500 Subject: [PATCH 07/11] Images Signed-off-by: Vineeth Kalluru --- .../imgs/intermediate_steps.png | Bin 0 -> 315589 bytes .../imgs/test_prompt_1.png | Bin 0 -> 442394 bytes .../imgs/test_prompt_2.png | Bin 0 -> 394191 bytes .../imgs/test_prompt_3.png | Bin 0 -> 448987 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 industries/manufacturing/predictive_maintenance_agent/imgs/intermediate_steps.png create mode 100644 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zU+CYV(SHJsh$P{6;w;tw{xI7O$S-S${;&QMM*savjuEJy8g+~U|5IoF^U8k>1tT)7 z6;$_6xcTP;VD>U)_~oeX+J-(?``;gi5pwV_KTymf{$H-d8CU@Y-|1gN{#gP4z7TvS rJ>b@_JPpwQ>u3J|&HX<#a*qgNkGWDjiif|R0DrPlN|F`g1_A#EKCwSu literal 0 HcmV?d00001 diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml index d340a7f1b..70b9cf40b 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml @@ -38,24 +38,16 @@ functions: vector_store_path: "${PWD_PATH}/database" db_path: "${PWD_PATH}/database/nasa_turbo.db" output_folder: "${PWD_PATH}/output_data" - predict_rul: - _type: predict_rul_tool - output_folder: "${PWD_PATH}/output_data" - scaler_path: "${PWD_PATH}/models/scaler_model.pkl" - model_path: "${PWD_PATH}/models/xgb_model_fd001.pkl" plot_distribution: _type: plot_distribution_tool output_folder: "${PWD_PATH}/output_data" - plot_comparison: - _type: plot_comparison_tool - output_folder: "${PWD_PATH}/output_data" plot_line_chart: _type: plot_line_chart_tool output_folder: "${PWD_PATH}/output_data" plotting_agent: _type: react_agent llm_name: nim_llm - tool_names: [plot_line_chart, plot_comparison, plot_distribution] + tool_names: [plot_line_chart, plot_distribution] verbose: true handle_tool_errors: true description: "Use this agent to generate plots from the retrieved SQL data. Provide a description of the plot required along with the JSON file path containing the SQL output in the input message. @@ -67,7 +59,6 @@ workflow: tool_names: - sql_retriever - plotting_agent - - predict_rul verbose: true system_prompt: | You are a conversational helpful assistant that can help with predictive maintenance tasks for a turbofan engine. From 1ee76b39634f72e2b77dc0689021d7d4007f7e8e Mon Sep 17 00:00:00 2001 From: Vineeth Kalluru Date: Mon, 16 Jun 2025 14:22:00 -0500 Subject: [PATCH 10/11] EOL fixes Signed-off-by: Vineeth Kalluru --- .gitignore | 2 +- .../manufacturing/predictive_maintenance_agent/pyproject.toml | 2 +- .../generate_sql_query_and_retrieve_tool.py | 2 +- .../src/predictive_maintenance_agent/plot_comparison_tool.py | 2 +- .../src/predictive_maintenance_agent/plot_distribution_tool.py | 2 +- .../src/predictive_maintenance_agent/plot_line_chart_tool.py | 2 +- .../src/predictive_maintenance_agent/predict_rul_tool.py | 2 +- .../src/predictive_maintenance_agent/register.py | 2 +- 8 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.gitignore b/.gitignore index 3cd415db0..1d331c5c1 100644 --- a/.gitignore +++ b/.gitignore @@ -37,4 +37,4 @@ RAG/notebooks/langchain/data/save_embedding .env # egg-info directories -**/egg-info \ No newline at end of file +**/egg-info diff --git a/industries/manufacturing/predictive_maintenance_agent/pyproject.toml b/industries/manufacturing/predictive_maintenance_agent/pyproject.toml index eeb0c3876..7efa09139 100644 --- a/industries/manufacturing/predictive_maintenance_agent/pyproject.toml +++ b/industries/manufacturing/predictive_maintenance_agent/pyproject.toml @@ -16,4 +16,4 @@ authors = [{ name = "Vineeth Kalluru" }] maintainers = [{ name = "NVIDIA Corporation" }] [project.entry-points.'aiq.components'] -predictive_maintenance_agent = "predictive_maintenance_agent.register" \ No newline at end of file +predictive_maintenance_agent = "predictive_maintenance_agent.register" diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py index 61f4e3c37..6b9d10486 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py @@ -247,4 +247,4 @@ async def _response_fn(input_question_in_english: str) -> str: except GeneratorExit: logger.info("Generate SQL query and retrieve function exited early!") finally: - logger.info("Cleaning up generate_sql_query_and_retrieve_tool workflow.") \ No newline at end of file + logger.info("Cleaning up generate_sql_query_and_retrieve_tool workflow.") diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py index 40d800915..5b43e856f 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py @@ -360,4 +360,4 @@ async def _response_fn(data_json_path: str, x_axis_column: str, y_axis_column_1: except GeneratorExit: logger.info("Plot comparison function exited early!") finally: - logger.info("Cleaning up plot_comparison_tool workflow.") \ No newline at end of file + logger.info("Cleaning up plot_comparison_tool workflow.") diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py index 7ef2df6d7..3c0a5f1a1 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py @@ -208,4 +208,4 @@ async def _response_fn(data_json_path: str, column_name: str, plot_title: str) - except GeneratorExit: logger.info("Plot distribution function exited early!") finally: - logger.info("Cleaning up plot_distribution_tool workflow.") \ No newline at end of file + logger.info("Cleaning up plot_distribution_tool workflow.") diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py index 707f07bdc..4c1047c02 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py @@ -239,4 +239,4 @@ async def _response_fn(data_json_path: str, x_axis_column: str, y_axis_column: s except GeneratorExit: logger.info("Plot line chart function exited early!") finally: - logger.info("Cleaning up plot_line_chart_tool workflow.") \ No newline at end of file + logger.info("Cleaning up plot_line_chart_tool workflow.") diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py index c4450bbc1..e236545ee 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py @@ -248,4 +248,4 @@ async def _response_fn(json_file_path: str) -> str: except GeneratorExit: logger.info("Predict RUL function exited early!") finally: - logger.info("Cleaning up predict_rul_tool workflow.") \ No newline at end of file + logger.info("Cleaning up predict_rul_tool workflow.") diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/register.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/register.py index 128acf4ee..53df6e30f 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/register.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/register.py @@ -13,4 +13,4 @@ # from . import distribution_histogram_rul_tool # from . import plot_prediction_errors_tool # from . import plot_rul_error_by_health_state_tool -# from . import generate_chart_tool \ No newline at end of file +# from . import generate_chart_tool From ae1386dfa57b93eadbd478df0d2c0154a2313a93 Mon Sep 17 00:00:00 2001 From: Vineeth Kalluru Date: Mon, 30 Jun 2025 13:37:40 -0500 Subject: [PATCH 11/11] config files Signed-off-by: Vineeth Kalluru --- .../configs/config-reasoning.yml | 2 +- .../configs/config.yml | 82 +++++++++++++++++- .../configs/config.yml | 84 ------------------- 3 files changed, 82 insertions(+), 86 deletions(-) rename industries/manufacturing/predictive_maintenance_agent/{src/predictive_maintenance_agent => }/configs/config-reasoning.yml (99%) mode change 120000 => 100644 industries/manufacturing/predictive_maintenance_agent/configs/config.yml delete mode 100644 industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config-reasoning.yml b/industries/manufacturing/predictive_maintenance_agent/configs/config-reasoning.yml similarity index 99% rename from industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config-reasoning.yml rename to industries/manufacturing/predictive_maintenance_agent/configs/config-reasoning.yml index 7086b9dd1..7dce337fe 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config-reasoning.yml +++ b/industries/manufacturing/predictive_maintenance_agent/configs/config-reasoning.yml @@ -189,7 +189,7 @@ workflow: 4) Create comprehensive plots showing test data timeline with anomalies highlighted - Use different colors/markers to distinguish between normal data and show all different types of anomalies - Include hover information and legends for clear interpretation - - Save visualizations as interactive HTML files for detailed analysis + - Save visualizations as interactive HTML files for detailed analysis ---- diff --git a/industries/manufacturing/predictive_maintenance_agent/configs/config.yml b/industries/manufacturing/predictive_maintenance_agent/configs/config.yml deleted file mode 120000 index 113a15bec..000000000 --- a/industries/manufacturing/predictive_maintenance_agent/configs/config.yml +++ /dev/null @@ -1 +0,0 @@ -/Users/vikalluru/Documents/GenerativeAIExamples/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml \ No newline at end of file diff --git a/industries/manufacturing/predictive_maintenance_agent/configs/config.yml b/industries/manufacturing/predictive_maintenance_agent/configs/config.yml new file mode 100644 index 000000000..176efc363 --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/configs/config.yml @@ -0,0 +1,81 @@ +general: + use_uvloop: true + telemetry: + logging: + console: + _type: console + level: DEBUG + tracing: + phoenix: + _type: phoenix + endpoint: http://localhost:6006/v1/traces + project: predictive-maintenance-app + +llms: + sql_llm: + _type: nim + model_name: "meta/llama-4-scout-17b-16e-instruct" + plotting_llm: + _type: nim + model_name: "meta/llama-4-scout-17b-16e-instruct" + nim_llm: + _type: nim + model_name: "meta/llama-4-scout-17b-16e-instruct" + +embedders: + vanna_embedder: + _type: nim + model_name: "nvidia/nv-embed-v1" + +functions: + sql_retriever: + _type: generate_sql_query_and_retrieve_tool + llm_name: sql_llm + embedding_name: vanna_embedder + vector_store_path: "${PWD_PATH}/database" + db_path: "${PWD_PATH}/database/nasa_turbo.db" + output_folder: "${PWD_PATH}/output_data" + plot_distribution: + _type: plot_distribution_tool + output_folder: "${PWD_PATH}/output_data" + plot_line_chart: + _type: plot_line_chart_tool + output_folder: "${PWD_PATH}/output_data" + plotting_agent: + _type: react_agent + llm_name: nim_llm + tool_names: [plot_line_chart, plot_distribution] + verbose: true + handle_tool_errors: true + description: "Use this agent to generate plots from the retrieved SQL data. Provide a description of the plot required along with the JSON file path containing the SQL output in the input message. + for example: Plot the sensor_measurement_1 vs time_in_cycles for unit 1 from the file ${PWD_PATH}/output_data/sql_output.json" + +workflow: + _type: react_agent + llm_name: nim_llm + tool_names: + - sql_retriever + - plotting_agent + verbose: true + system_prompt: | + You are a conversational helpful assistant that can help with predictive maintenance tasks for a turbofan engine. + Answer the user's question as best as you can while not doing more than what is asked. You can optionally use the following tools to help with your task: + {tools} + Not all queries require you to use the tools, only use them if you need to. + If a tool returns an HTML file, send it to the user. + DO NOT GENERATE SQL QUERIES BY YOURSELF, ONLY USE THE TOOLS!!! + You may respond in one of two formats: + + Use the following format exactly when you want to use a tool: + + Question: the input question you must answer + Thought: you should always think about what to do + Action: the action to take, should be one of [{tool_names}] + Action Input: the input to the action (if there is no required input, include "Action Input: None") + Observation: wait for the tool to finish execution + + Use the following format exactly when you don't want to use a tool: + + Question: the input question you must answer + Thought: you should always think about what to do + Final Answer: the final answer to the original input question diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml deleted file mode 100644 index 70b9cf40b..000000000 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml +++ /dev/null @@ -1,84 +0,0 @@ -general: - use_uvloop: true - telemetry: - logging: - console: - _type: console - level: DEBUG - tracing: - phoenix: - _type: phoenix - endpoint: http://localhost:6006/v1/traces - project: predictive-maintenance-app - -llms: - sql_llm: - _type: nim - model_name: "meta/llama-4-scout-17b-16e-instruct" - plotting_llm: - _type: nim - model_name: "meta/llama-4-scout-17b-16e-instruct" - nim_llm: - _type: nim - model_name: "meta/llama-4-scout-17b-16e-instruct" - reasoning_llm: - _type: nim - model_name: "deepseek-ai/deepseek-r1-distill-qwen-32b" - -embedders: - vanna_embedder: - _type: nim - model_name: "nvidia/nv-embed-v1" - -functions: - sql_retriever: - _type: generate_sql_query_and_retrieve_tool - llm_name: sql_llm - embedding_name: vanna_embedder - vector_store_path: "${PWD_PATH}/database" - db_path: "${PWD_PATH}/database/nasa_turbo.db" - output_folder: "${PWD_PATH}/output_data" - plot_distribution: - _type: plot_distribution_tool - output_folder: "${PWD_PATH}/output_data" - plot_line_chart: - _type: plot_line_chart_tool - output_folder: "${PWD_PATH}/output_data" - plotting_agent: - _type: react_agent - llm_name: nim_llm - tool_names: [plot_line_chart, plot_distribution] - verbose: true - handle_tool_errors: true - description: "Use this agent to generate plots from the retrieved SQL data. Provide a description of the plot required along with the JSON file path containing the SQL output in the input message. - for example: Plot the sensor_measurement_1 vs time_in_cycles for unit 1 from the file ${PWD_PATH}/output_data/sql_output.json" - -workflow: - _type: react_agent - llm_name: nim_llm - tool_names: - - sql_retriever - - plotting_agent - verbose: true - system_prompt: | - You are a conversational helpful assistant that can help with predictive maintenance tasks for a turbofan engine. - Answer the user's question as best as you can while not doing more than what is asked. You can optionally use the following tools to help with your task: - {tools} - Not all queries require you to use the tools, only use them if you need to. - If a tool returns an HTML file, send it to the user. - DO NOT GENERATE SQL QUERIES BY YOURSELF, ONLY USE THE TOOLS!!! - You may respond in one of two formats: - - Use the following format exactly when you want to use a tool: - - Question: the input question you must answer - Thought: you should always think about what to do - Action: the action to take, should be one of [{tool_names}] - Action Input: the input to the action (if there is no required input, include "Action Input: None") - Observation: wait for the tool to finish execution - - Use the following format exactly when you don't want to use a tool: - - Question: the input question you must answer - Thought: you should always think about what to do - Final Answer: the final answer to the original input question

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z5bh82$2rweSj{d!T$L!% z3tvQgX{xJc;9wky|3cE-{gksdSuW0M(Kss8exv8F-9^z34rm1Jv~d4zmXLs}$8sIuG6Fx-y_9l7m| ze|gQ-%Y1{Pwx2~NM?yE%muv&58UR&=$|llqzSDi;?t%w`Gw@*G*>yy_@yJpozG9XR z*Ih6fzTgr_NQfJVua*A>Adjwr`D-#0b;LL=VzAf26x!|NllPFAvJ!YM-v7~J5$FSr zfe=Yt5z=2v0z25L&TUTBve825GoSd|z?S%~h^_Fh#TA|(4`y+79dDnj9bNBOdIE+w zI77c+q`7ekJ4WI7Qv$+*<)srT*}wh166cWm@GfYtAR z3vugC!EyUVFpmzW6TRSW7)XE29(*{^6qg^MkJ`~O*)ZSf9g~p$FYn50nYJ{HUIzs literal 0 HcmV?d00001 From eea69e581640ea705a390ac475539606a4368510 Mon Sep 17 00:00:00 2001 From: Vineeth Kalluru Date: Wed, 11 Jun 2025 23:32:17 -0700 Subject: [PATCH 08/11] Reasoning based workflow complete Signed-off-by: Vineeth Kalluru --- .../configs/config-reasoning.yml | 207 ++++++++++++++++++ .../configs/config.yml | 1 - .../generate_sql_query_and_retrieve_tool.py | 70 ++++-- .../plot_comparison_tool.py | 17 +- .../plot_distribution_tool.py | 16 +- .../plot_line_chart_tool.py | 14 +- .../predict_rul_tool.py | 14 +- 7 files changed, 296 insertions(+), 43 deletions(-) create mode 100644 industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config-reasoning.yml diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config-reasoning.yml b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config-reasoning.yml new file mode 100644 index 000000000..7086b9dd1 --- /dev/null +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config-reasoning.yml @@ -0,0 +1,207 @@ +general: + use_uvloop: true + telemetry: + logging: + console: + _type: console + level: DEBUG + tracing: + phoenix: + _type: phoenix + endpoint: http://localhost:6006/v1/traces + project: predictive-maintenance-app + +llms: + sql_llm: + _type: nim + model_name: "meta/llama-4-scout-17b-16e-instruct" + coding_llm: + _type: nim + model_name: "qwen/qwen2.5-coder-32b-instruct" + max_tokens: 2000 + reasoning_llm: + _type: nim + model_name: "qwen/qwq-32b" + +embedders: + vanna_embedder: + _type: nim + model_name: "nvidia/nv-embed-v1" + +functions: + sql_retriever: + _type: generate_sql_query_and_retrieve_tool + llm_name: sql_llm + embedding_name: vanna_embedder + vector_store_path: "${PWD_PATH}/database" + db_path: "${PWD_PATH}/database/nasa_turbo.db" + output_folder: "${PWD_PATH}/output_data" + predict_rul: + _type: predict_rul_tool + output_folder: "${PWD_PATH}/output_data" + scaler_path: "${PWD_PATH}/models/scaler_model.pkl" + model_path: "${PWD_PATH}/models/xgb_model_fd001.pkl" + code_execution: + _type: code_execution + uri: http://127.0.0.1:6000/execute + sandbox_type: local + max_output_characters: 2000 + data_analysis_assistant: + _type: react_agent + llm_name: coding_llm + max_iterations: 5 + tool_names: [sql_retriever, code_execution, predict_rul] + system_prompt: | + You are a helpful data analysis assistant that can help with predictive maintenance tasks for a turbofan engine. You will work with planning agent + that provides a plan to you which you should follow: + You can use the following tools to help with your task: + {tools} + + Note: Your output_data folder is in "${PWD_PATH}/output_data" path. + However, the code execution sandbox runs with /workspace as the working directory (mounted to your local output_data folder) + So, when you are using the code execution tool, you should use relative paths starting with './' for file operations. + For example, if you want to read a file from the output_data folder, you should use './filename.json' as the path. + + But when passing any generated JSON file to other tools, you should use the absolute path to the file. + For example, if you want to pass the file 'engine_unit_24_sensor_data.json' to the predict_rul tool, you should use the absolute path to the file. + which is "${PWD_PATH}/output_data/engine_unit_24_sensor_data.json" + + **EXAMPLE CODE STRUCTURE:** + ### START PYTHON CODE ### + import pandas as pd + import plotly.graph_objects as go + + # Load data using relative path (working directory is /workspace mounted to your output_data) + data = pd.read_json('your_input_file.json') + + # Create your analysis/plot + fig = go.Figure(data=[go.Scatter(x=data['time_in_cycles'], y=data['sensor_measurement_10'])]) + fig.update_layout(title='Your Plot Title') + + # Save to current directory (will appear in your local output_data folder) + fig.write_html('your_output_file.html') + print(f"Plot saved to: your_output_file.html") + ### END PYTHON CODE ### + + # File Handling and Tool Usage Guidelines + # -------------------------------- + # 1. HTML File Paths + # - All HTML files from code execution are saved in the output_data directory + # - Always include the full path when referencing HTML files to users + # - Example: "${PWD_PATH}/output_data/plot_name.html" + + # 2. SQL Query Policy + # - NEVER generate SQL queries manually + # - ALWAYS use the provided SQL retrieval tool + + # 3. Typical Workflow + # a) Data Extraction + # - Use SQL retrieval tool to fetch required data + # b) Data Processing + # - Generate Python code for analysis/visualization + # - Execute code using code execution tool + # - Save results in output_data directory + # c) Result Handling + # - Return processed information to calling agent + # - DO NOT USE MARKDOWN FORMATTING IN YOUR RESPONSE. + # - If the code execution tool responds with a warning in the stderr then ignore it and take action based on the stdout. + + # 4. Visualization Guidelines + # - Use plotly.js for creating interactive plots + # - Save visualizations as HTML files + # - Store all plots in output_data directory + # - When comparing actual and predicted RUL columns, convert the actual RUL column to piecewise its piecewise RUL values before plotting. + Piecewise RUL instructions: + 1) Calculate the true failure point by taking the last cycle in your data and adding the final RUL value at that cycle (e.g., if last cycle is 100 with RUL=25, true failure is at cycle 125). + 2) Create the piecewise pattern where if the true failure cycle is greater than MAXLIFE (125), RUL stays flat at MAXLIFE until the "knee point" (true_failure - MAXLIFE), then declines linearly to zero; otherwise RUL just declines linearly from MAXLIFE. + 3) Generate RUL values for each cycle in your data using this pattern - flat section gets constant MAXLIFE value, declining section decreases by (MAXLIFE / remaining_cycles_to_failure) each step. + 4) Replace the actual RUL column in your dataset with these calculated piecewise values while keeping all other columns unchanged. + 5) The result is a "knee-shaped" RUL curve that better represents equipment degradation patterns - flat during early life, then linear decline toward failure. + + You may respond in one of two formats: + + Use the following format exactly when you want to use a tool: + + Question: the input question you must answer + Thought: you should always think about what to do + Action: the action to take, should be one of [{tool_names}] + Action Input: the input to the action (if there is no required input, include "Action Input: None") + Observation: wait for the tool to finish execution + + Use the following format exactly when you don't want to use a tool: + + Question: the input question you must answer + Thought: you should always think about what to do + Final Answer: the final answer to the original input question + + Use only the SQL retrieval tool for fetching data, do not generate code to do that. + +workflow: + _type: reasoning_agent + augmented_fn: data_analysis_assistant + llm_name: reasoning_llm + verbose: true + reasoning_prompt_template: | + You are a Data Analysis Reasoning and Planning Expert specialized in analyzing turbofan engine sensor data and predictive maintenance tasks. + You are tasked with creating detailed execution plans for addressing user queries while being conversational and helpful. + + **Your Role and Capabilities:** + - Expert in turbofan engine data analysis, predictive maintenance, and anomaly detection + - Create step-by-step execution plans using available tools + - Provide conversational responses while maintaining technical accuracy + - Only use tools when necessary to answer the user's question + + You are given a data analysis assistant to execute your plan, all you have to do is generate the plan. + DO NOT USE MARKDOWN FORMATTING IN YOUR RESPONSE. + + **Description:** + {augmented_function_desc} + + **Tools and description of the tool:** {tools} + + Guidelines: + 1. **Send the path to any HTML files generated to users** when tools return them (especially plotting results) + 2. **Only use tools if needed** - Not all queries require tool usage + + ---- + + Necessary Context: + You work with turbofan engine sensor data from multiple engines in a fleet. The data contains: + - **Time series data** from different engines, each with unique wear patterns and operational history separated into + four datasets (FD001, FD002, FD003, FD004), each dataset is further divided into training and test subsets. + - **26 data columns**: unit number, time in cycles, 3 operational settings, and 21 sensor measurements + - **Engine lifecycle**: Engines start operating normally, then develop faults that grow until system failure + - **Predictive maintenance goal**: Predict Remaining Useful Life (RUL) - how many operational cycles before failure + - **Data characteristics**: Contains normal operational variation, sensor noise, and progressive fault development + This context helps you understand user queries about engine health, sensor patterns, failure prediction, and maintenance planning. + + For Anomaly Detection Tasks: + When performing anomaly detection, follow this comprehensive approach: + + 1) First get the sensor measurement information for the same engine number from both training and test datasets across different cycle times and order + it in increasing order. + 2) Use the measurement from training data to calculate statistical baselines (mean, standard deviation, moving averages), because it represents what + normal operational behvaior looks like. + 3) Apply multiple statistical approaches to identify anomalies in test data: + - **Z-Score Analysis**: Compare test values against training data mean/std deviation using threshold (typically 3) + - **Moving Statistical Analysis**: Use rolling windows from training data to detect dynamic anomalies + - Flag data points that exceed statistical thresholds as potential anomalies + 4) Create comprehensive plots showing test data timeline with anomalies highlighted + - Use different colors/markers to distinguish between normal data and show all different types of anomalies + - Include hover information and legends for clear interpretation + - Save visualizations as interactive HTML files for detailed analysis + + ---- + + **User Input:** + {input_text} + + Analyze the input and create a comprehensive plan following this structure: + + Generate a plan that would look like this with numbered bullet points: + 1. Call tool A with input X + 2. Call tool B with input Y + 3. Interpret the output of tool A and B + 4. Return the final result + + **PLAN:** diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml index 6f42fc3d7..d340a7f1b 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/configs/config.yml @@ -58,7 +58,6 @@ functions: tool_names: [plot_line_chart, plot_comparison, plot_distribution] verbose: true handle_tool_errors: true - max_iterations: 1 description: "Use this agent to generate plots from the retrieved SQL data. Provide a description of the plot required along with the JSON file path containing the SQL output in the input message. for example: Plot the sensor_measurement_1 vs time_in_cycles for unit 1 from the file ${PWD_PATH}/output_data/sql_output.json" diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py index 0d0e8dff9..61f4e3c37 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/generate_sql_query_and_retrieve_tool.py @@ -3,7 +3,7 @@ import os from aiq.builder.framework_enum import LLMFrameworkEnum -from pydantic import Field +from pydantic import Field, BaseModel from aiq.builder.builder import Builder from aiq.builder.function_info import FunctionInfo @@ -16,7 +16,7 @@ class GenerateSqlQueryAndRetrieveToolConfig(FunctionBaseConfig, name="generate_s """ AIQ Toolkit function to generate SQL queries and retrieve data. """ - # Add your custom configuration parameters here + # Runtime configuration parameters llm_name: str = Field(description="The name of the LLM to use for the function.") embedding_name: str = Field(description="The name of the embedding to use for the function.") vector_store_path: str = Field(description="The path to the vector store to use for the function.") @@ -30,6 +30,9 @@ async def generate_sql_query_and_retrieve_tool( """ Generate a SQL query for a given question and retrieve the data from the database. """ + class GenerateSqlQueryInputSchema(BaseModel): + input_question_in_english: str = Field(description="User's question in plain English to generate SQL query for") + # Create Vanna instance vanna_llm_config = builder.get_llm_config(config.llm_name) vanna_embedder_config = builder.get_embedder_config(config.embedding_name) @@ -46,12 +49,14 @@ async def generate_sql_query_and_retrieve_tool( 2. For data extraction queries (multiple rows/complex data): recommend saving to JSON file and provide summary 3. For simple queries (single values, counts, yes/no): provide direct answers without file storage 4. Always be helpful and provide context about the results + 5. Generate a descriptive filename for data that should be saved Guidelines: - If results contain multiple rows or complex data (>5 rows or >3 columns): recommend saving to file - If results are simple (single value, count, or small lookup): provide direct answer - Always mention the SQL query that was executed - Be clear about whether data was saved to a file or not + - For files to be saved, suggest a descriptive filename based on the query content (e.g., "sensor_data_unit_5.json", "engine_performance_analysis.json") """ user_prompt = """ @@ -65,13 +70,16 @@ async def generate_sql_query_and_retrieve_tool( - Columns: {columns} - Sample data (first few rows): {sample_data} - File Path (if data should be saved): {file_path} + Output directory: {output_dir} Please provide an appropriate response that either: - 1. Saves the data to JSON file and provides a summary (for complex/large datasets) + 1. Saves the data to JSON file and provides a summary (for complex/large datasets) - suggest a descriptive filename 2. Directly answers the question with the results (for simple queries) Be conversational and helpful. Explain what was found and next steps if applicable. + If saving data, suggest a meaningful filename in the format: "descriptive_name.json" + + Important: Do not use template variables or placeholders in your response. Provide actual values and descriptions. """ prompt = ChatPromptTemplate.from_messages([("system", system_prompt), ("user", user_prompt)]) @@ -126,14 +134,14 @@ def get_vanna_instance(vanna_llm_config, vanna_embedder_config, vector_store_pat vn_instance = get_vanna_instance(vanna_llm_config, vanna_embedder_config, config.vector_store_path, config.db_path) - async def _response_fn(input_message: str) -> str: - # Process the input_message and generate output + async def _response_fn(input_question_in_english: str) -> str: + # Process the input_question_in_english and generate output if vn_instance is None: return "Error: Vanna instance not available" sql = None try: - sql = vn_instance.generate_sql(question=input_message) + sql = vn_instance.generate_sql(question=input_question_in_english) logger.info(f"Generated SQL: {sql}") except Exception as e: return f"Error generating SQL: {e}" @@ -155,18 +163,15 @@ async def _response_fn(input_message: str) -> str: # Get sample data (first 3 rows for preview) sample_data = df.head(3).to_dict('records') - # Prepare file path for potential saving - sql_output_path = os.path.join(config.output_folder, "sql_output.json") - # Use LLM to generate intelligent response response = await output_message.ainvoke({ - "original_question": input_message, + "original_question": input_question_in_english, "sql_query": sql, "num_rows": num_rows, "num_columns": num_columns, "columns": ", ".join(columns), "sample_data": json.dumps(sample_data, indent=2), - "file_path": sql_output_path + "output_dir": config.output_folder }) # Check if LLM response suggests saving data (look for keywords or patterns) @@ -182,6 +187,25 @@ async def _response_fn(input_message: str) -> str: ) if should_save_data: + # Extract suggested filename from LLM response or use default + import re + filename_match = re.search(r'"([^"]+\.json)"', llm_response) + if filename_match: + suggested_filename = filename_match.group(1) + else: + # Generate a descriptive filename based on the question + import hashlib + # Clean the question for filename + clean_question = re.sub(r'[^\w\s-]', '', input_question_in_english.lower()) + clean_question = re.sub(r'\s+', '_', clean_question.strip())[:30] + if clean_question: + suggested_filename = f"{clean_question}_results.json" + else: + query_hash = hashlib.md5(input_question_in_english.encode()).hexdigest()[:8] + suggested_filename = f"sql_results_{query_hash}.json" + + sql_output_path = os.path.join(config.output_folder, suggested_filename) + # Save the data to JSON file os.makedirs(config.output_folder, exist_ok=True) json_result = df.to_json(orient="records") @@ -189,9 +213,17 @@ async def _response_fn(input_message: str) -> str: json.dump(json.loads(json_result), f, indent=4) logger.info(f"Data saved to {sql_output_path}") - # If LLM didn't mention saving, append save confirmation - if "saved" not in llm_response.lower(): - llm_response += f"\n\n📁 Data has been saved to: {sql_output_path} with the following columns: {', '.join(columns)}" + # Clean up the LLM response and add file save confirmation + # Remove any object references that might have slipped through + cleaned_response = re.sub(r',\[object Object\],?', '', llm_response) + cleaned_response = re.sub(r'\[object Object\]', str(num_rows), cleaned_response) + + # If LLM didn't mention the actual saved path, append save confirmation + if sql_output_path not in cleaned_response: + cleaned_response += f"\n\n📁 Data has been saved to: {sql_output_path}" + cleaned_response += f"\n📊 File contains {num_rows} rows with columns: {', '.join(columns)}" + + return cleaned_response return llm_response @@ -201,10 +233,14 @@ async def _response_fn(input_message: str) -> str: description = """ Use this tool to automatically generate SQL queries for the user's question, retrieve the data from the SQL database and store the data in a JSON file or provide a summary of the data. Do not provide SQL query as input, only a question in plain english. - Input: User's question or a question that you think is relevant to the user's question in plain english. - Output: Status of the generated SQL query's execution. + + Input: + - input_question_in_english: User's question or a question that you think is relevant to the user's question in plain english + + Output: Status of the generated SQL query's execution along with the output path. The tool will automatically generate descriptive filenames for saved data. """ yield FunctionInfo.from_fn(_response_fn, + input_schema=GenerateSqlQueryInputSchema, description=description) try: pass diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py index 5f272cc30..40d800915 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_comparison_tool.py @@ -3,7 +3,7 @@ import os import pandas as pd -from pydantic import Field +from pydantic import Field, BaseModel from aiq.builder.builder import Builder from aiq.builder.function_info import FunctionInfo @@ -120,17 +120,19 @@ class PlotComparisonToolConfig(FunctionBaseConfig, name="plot_comparison_tool"): """ AIQ Toolkit function to plot comparison of two y-axis columns against an x-axis column. """ - # Configuration parameters - # x_axis_column: str = Field(description="The column name for x-axis data.", default="time_in_cycles") - # y_axis_column_1: str = Field(description="The first column name for y-axis data.", default="actual_RUL") - # y_axis_column_2: str = Field(description="The second column name for y-axis data.", default="predicted_RUL") - # plot_title: str = Field(description="The title for the plot.", default="Comparison Plot") output_folder: str = Field(description="The path to the output folder to save plots.", default="./output_data") @register_function(config_type=PlotComparisonToolConfig) async def plot_comparison_tool( config: PlotComparisonToolConfig, builder: Builder ): + class PlotComparisonInputSchema(BaseModel): + data_json_path: str = Field(description="The path to the JSON file containing the data") + x_axis_column: str = Field(description="The column name for x-axis data", default="time_in_cycles") + y_axis_column_1: str = Field(description="The first column name for y-axis data", default="actual_RUL") + y_axis_column_2: str = Field(description="The second column name for y-axis data", default="predicted_RUL") + plot_title: str = Field(description="The title for the plot", default="Comparison Plot") + def load_data_from_json(json_path: str): """Load data from JSON file into a pandas DataFrame.""" import pandas as pd @@ -310,7 +312,7 @@ async def _response_fn(data_json_path: str, x_axis_column: str, y_axis_column_1: # Add info about RUL transformation if applied rul_info = "" - if y_axis_column_1 == "RUL" or y_axis_column_2 == "RUL": + if y_axis_column_1 == "actual_RUL" or y_axis_column_2 == "actual_RUL": rul_info = f"\n- Piecewise RUL transformation applied (max_life=125)" # Return a clear completion message that the LLM will understand @@ -351,6 +353,7 @@ async def _response_fn(data_json_path: str, x_axis_column: str, y_axis_column_1: - HTML file containing the comparison plot """ yield FunctionInfo.from_fn(_response_fn, + input_schema=PlotComparisonInputSchema, description=prompt) try: pass diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py index e4499e44c..7ef2df6d7 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_distribution_tool.py @@ -2,7 +2,7 @@ import logging import os -from pydantic import Field +from pydantic import Field, BaseModel from aiq.builder.builder import Builder from aiq.builder.function_info import FunctionInfo @@ -47,16 +47,17 @@ class PlotDistributionToolConfig(FunctionBaseConfig, name="plot_distribution_too """ AIQ Toolkit function to plot distribution histogram of a specified column. """ - # Configuration parameters - data_json_path: str = Field(description="The path to the JSON file containing the data.", default="") - column_name: str = Field(description="The column name to create distribution plot for.", default="RUL") - plot_title: str = Field(description="The title for the plot.", default="Distribution Plot") output_folder: str = Field(description="The path to the output folder to save plots.", default="./output_data") @register_function(config_type=PlotDistributionToolConfig) async def plot_distribution_tool( config: PlotDistributionToolConfig, builder: Builder ): + class PlotDistributionInputSchema(BaseModel): + data_json_path: str = Field(description="The path to the JSON file containing the data") + column_name: str = Field(description="The column name to create distribution plot for", default="RUL") + plot_title: str = Field(description="The title for the plot", default="Distribution Plot") + def load_data_from_json(json_path: str): """Load data from JSON file into a pandas DataFrame.""" import pandas as pd @@ -160,8 +161,8 @@ async def _response_fn(data_json_path: str, column_name: str, plot_title: str) - if df is None or df.empty: return "Could not load data or data is empty from the provided JSON file" - if config.column_name not in df.columns: - return f"Column '{config.column_name}' not found in data. Available columns: {df.columns.tolist()}" + if column_name not in df.columns: + return f"Column '{column_name}' not found in data. Available columns: {df.columns.tolist()}" output_filepath = create_distribution_plot_html( output_dir=config.output_folder, @@ -200,6 +201,7 @@ async def _response_fn(data_json_path: str, column_name: str, plot_title: str) - - HTML file containing the distribution histogram """ yield FunctionInfo.from_fn(_response_fn, + input_schema=PlotDistributionInputSchema, description=prompt) try: pass diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py index 2f324f8a7..707f07bdc 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/plot_line_chart_tool.py @@ -2,7 +2,7 @@ import logging import os -from pydantic import Field +from pydantic import Field, BaseModel from aiq.builder.builder import Builder from aiq.builder.function_info import FunctionInfo @@ -47,17 +47,18 @@ class PlotLineChartToolConfig(FunctionBaseConfig, name="plot_line_chart_tool"): """ AIQ Toolkit function to plot a line chart with specified x and y axis columns. """ - # Configuration parameters - # data_json_path: str = Field(description="The path to the JSON file containing the data.", default="./output_data/sql_output.json") - # x_axis_column: str = Field(description="The column name for x-axis data.", default="time_in_cycles") - # y_axis_column: str = Field(description="The column name for y-axis data.", default="RUL") - # plot_title: str = Field(description="The title for the plot.", default="Line Chart") output_folder: str = Field(description="The path to the output folder to save plots.", default="./output_data") @register_function(config_type=PlotLineChartToolConfig) async def plot_line_chart_tool( config: PlotLineChartToolConfig, builder: Builder ): + class PlotLineChartInputSchema(BaseModel): + data_json_path: str = Field(description="The path to the JSON file containing the data") + x_axis_column: str = Field(description="The column name for x-axis data", default="time_in_cycles") + y_axis_column: str = Field(description="The column name for y-axis data", default="RUL") + plot_title: str = Field(description="The title for the plot", default="Line Chart") + def load_data_from_json(json_path: str): """Load data from JSON file into a pandas DataFrame.""" import pandas as pd @@ -231,6 +232,7 @@ async def _response_fn(data_json_path: str, x_axis_column: str, y_axis_column: s - HTML file containing the line chart """ yield FunctionInfo.from_fn(_response_fn, + input_schema=PlotLineChartInputSchema, description=prompt) try: pass diff --git a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py index 9d3cbb69f..c4450bbc1 100644 --- a/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py +++ b/industries/manufacturing/predictive_maintenance_agent/src/predictive_maintenance_agent/predict_rul_tool.py @@ -6,7 +6,7 @@ import joblib import numpy as np -from pydantic import Field +from pydantic import Field, BaseModel from aiq.builder.builder import Builder from aiq.builder.function_info import FunctionInfo @@ -51,7 +51,7 @@ class PredictRulToolConfig(FunctionBaseConfig, name="predict_rul_tool"): """ AIQ Toolkit function to predict RUL (Remaining Useful Life) using trained models and provided data. """ - # Configuration parameters + # Runtime configuration parameters scaler_path: str = Field(description="Path to the trained StandardScaler model.", default="./models/scaler_model.pkl") model_path: str = Field(description="Path to the trained XGBoost model.", default="./models/xgb_model_fd001.pkl") output_folder: str = Field(description="The path to the output folder to save prediction results.", default="./output_data") @@ -60,6 +60,9 @@ class PredictRulToolConfig(FunctionBaseConfig, name="predict_rul_tool"): async def predict_rul_tool( config: PredictRulToolConfig, builder: Builder ): + class PredictRulInputSchema(BaseModel): + json_file_path: str = Field(description="Path to a JSON file containing sensor measurements data for RUL prediction") + def load_data_from_json(json_path: str): """Load data from JSON file into a pandas DataFrame.""" import pandas as pd @@ -156,12 +159,12 @@ def predict_rul_from_data(data_json_path: str, scaler_path: str, model_path: str return y_pred, data_json_path - async def _response_fn(input_message: str) -> str: + async def _response_fn(json_file_path: str) -> str: """ Process the input message and generate RUL predictions using trained models. """ - logger.info(f"Input message: {input_message}") - data_json_path = verify_json_path(input_message) + logger.info(f"Input message: {json_file_path}") + data_json_path = verify_json_path(json_file_path) try: predictions, output_filepath = predict_rul_from_data( data_json_path=data_json_path, @@ -238,6 +241,7 @@ async def _response_fn(input_message: str) -> str: - Updated JSON file with predictions added as 'predicted_RUL' column """ yield FunctionInfo.from_fn(_response_fn, + input_schema=PredictRulInputSchema, description=prompt) try: pass From fb984ab7d95a52971b04e51cecdc3f3ea064d40d Mon Sep 17 00:00:00 2001 From: Vineeth Kalluru Date: Mon, 16 Jun 2025 14:13:57 -0500 Subject: [PATCH 09/11] architecture diagram Signed-off-by: Vineeth Kalluru --- .../predictive_maintenance_agent/README.md | 17 +++++++++++++---- .../imgs/pred_maint_arch_diagram_img1.png | Bin 0 -> 104048 bytes .../imgs/pred_maint_arch_diagram_img2.png | Bin 0 -> 155781 bytes .../configs/config.yml | 11 +---------- 4 files changed, 14 insertions(+), 14 deletions(-) create mode 100644 industries/manufacturing/predictive_maintenance_agent/imgs/pred_maint_arch_diagram_img1.png create mode 100644 industries/manufacturing/predictive_maintenance_agent/imgs/pred_maint_arch_diagram_img2.png diff --git a/industries/manufacturing/predictive_maintenance_agent/README.md b/industries/manufacturing/predictive_maintenance_agent/README.md index 9b4f1f964..dea59b614 100644 --- a/industries/manufacturing/predictive_maintenance_agent/README.md +++ b/industries/manufacturing/predictive_maintenance_agent/README.md @@ -32,7 +32,11 @@ Multi-agent architecture with: - **Plotting Agent**: Multi-tool agent for data visualization - **Vector Database**: ChromaDB for schema information storage -![Architecture Workflow](imgs/intermediate_steps.png) +#### Agentic workflow architecture diagram +![Agentic workflow](imgs/pred_maint_arch_diagram_img1.png) + +#### Agentic workflow architecture diagram w/ reasoning +![Agentic workflow w/ reasoning](imgs/pred_maint_arch_diagram_img2.png) ## Setup and Installation @@ -99,12 +103,17 @@ Server runs on `http://localhost:8000` Note: When using the provided config file, you need to set the PWD_PATH environment variable before starting the AIQ server. This ensures the server can locate all required paths correctly. -Here's how to do it: +Here's how to do it: ```bash export PWD_PATH=$(pwd) aiq serve --config_file=configs/config.yml "$@" ``` +(or) +```bash +export PWD_PATH=$(pwd) +aiq serve --config_file=configs/config-reasoning.yml "$@" +``` ### Setup Web Interface @@ -127,14 +136,14 @@ Test the system with these prompts: **Data Retrieval:** ``` -Retrieve RUL of each unit from the FD001 dataset. Then plot the distribution of RUL. +Retrieve the time in cycles and operational setting 1 from the FD001 test table for unit number 1 and plot its value vs time. ``` ![Data Retrieval Example](imgs/test_prompt_1.png) **Visualization:** ``` -Retrieve the time in cycles and operational setting 1 from the FD001 test table for unit number 1 and plot its value vs time. +Retrieve real RUL of each unit in the FD001 test dataset. Then plot a distribution of it. ``` ![Visualization Example](imgs/test_prompt_2.png) diff --git a/industries/manufacturing/predictive_maintenance_agent/imgs/pred_maint_arch_diagram_img1.png b/industries/manufacturing/predictive_maintenance_agent/imgs/pred_maint_arch_diagram_img1.png new file mode 100644 index 0000000000000000000000000000000000000000..99d5293a85d9b23b8513adc73b871d3453a6651a GIT binary patch literal 104048 zcmc$G1zR22(lro*CAhmof;$9v39ccyySux)1P@M-;O_2D(1TlW55etgX2{IF_x%AM z&uLE6-P>yKs#UdWH=%MeV(@S{aA06y@Dk#}3SeNb^uWL%LSUePD=5$sU0`6~DyBk0 zauPy9L~?f4My3{qU|?7YmN9&i-EwHXvNdXjaQKJYF#>^t=%Hbpvv4FFJ-6j>2&O2C z#Fm5-!abvd5=eRR#bsfVcp?u_80hW3nt^Z4La$iO)Gm1FuCC~IG!dXp{JWB|!R2T^ zs5m*Pjg*##|G;!0iNX?%kyA?hX6Twhc70sh@9xua_kgszHA5k59bcx&voMfOxsvg? zuP!4sWtngbJ(D#=1C4YWx`iKXn=e={k{^P|feW5#GY0MPV}izzxyQN3wl>S8M|nf3 z&D4s;_Q#S+JGaf%vYV-E>|OSLH{4_lgD>{bmiMx%P?7-l+vbXj*RNi4Anj4+nhR29 z2*FYX#gZhxlZM^oUR`YrWIjhqUVvV>>Pk@oFEisG7v#Wg$h%^+$z|wO@R~$x32FU6 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