Add Local Ollama NLP Backend for Natural-Language Computed Fields

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+# Ollama NLP Backend for Custom Computed Fields - Full PR Description
+
+## Overview
+This PR adds a complete local LLM-powered backend service for generating SQL expressions for custom computed fields in Graphic Walker using Ollama. The service runs entirely on-premises with no external API dependencies, ensuring privacy, cost-efficiency, and offline capability.
+
+**📹 Includes demonstration video: [NLP-Based Compute Field](./packages/nlp-backend/NLP-Based-Compute-Field.mp4)**
+
+## Purpose
+Enable users to generate SQL computed field expressions from natural language descriptions using their local Ollama installation, providing a seamless integration between Graphic Walker's UI and local language models.
+
+## Key Features Implemented
+
+### 1. **Natural Language to SQL Conversion**
+- Local LLM processing via Ollama integration
+- Intelligent prompt engineering for proper SQL generation
+- Support for conditional logic (CASE WHEN...THEN...ELSE...END)
+- Field-aware SQL expression generation
+- Mathematical and arithmetic operations support
+
+### 2. **Advanced SQL Processing**
+- **Comprehensive SQL processor** (`sql_processor.py`):
+  - Markdown and code block removal
+  - Ollama-specific response pattern cleaning
+  - Model hallucination detection and filtering
+  - SQL validation and syntax checking
+  - Complex expression extraction from mixed content
+  - Configurable regex patterns for maximum flexibility
+
+### 3. **Robust Service Architecture**
+- **Ollama client** (`ollama_client.py`):
+  - Async/await support for non-blocking operations
+  - Model availability validation and fallback logic
+  - Graceful error handling with specific error categorization
+  - Health checking with timeout protection
+
+- **Service layer** (`ollama_service.py`):
+  - Request processing with comprehensive logging
+  - Error categorization (timeout, connection, model, validation errors)
+  - Detailed request tracking and monitoring
+
+- **Model management** (`model_manager.py`):
+  - Dynamic model selection and validation
+  - Fallback model support
+  - Model availability caching
+  - Recommended models identification
+
+### 4. **Health Monitoring & Observability**
+- **Health monitoring** (`health_monitor.py`):
+  - Comprehensive startup validation
+  - Multi-level health checks (connectivity, model availability, performance, resources)
+  - Health status tracking and history
+  - Degraded state detection
+
+- **Metrics collection** (`metrics_collector.py`):
+  - Request metrics tracking (response times, success rates, error types)
+  - Performance time series data collection
+  - Request history with filtering
+  - Detailed performance reports and trend analysis
+  - Thread-safe metrics collection
+
+- **Monitoring dashboard** (`monitoring_dashboard.py`):
+  - Real-time metrics aggregation
+  - Performance trend analysis
+  - Health status formatting
+  - Report generation (hourly, daily, weekly)
+  - Alert system for anomalies
+
+### 5. **Enterprise-Grade Logging**
+- **Structured logging** (`logging_config.py`):
+  - JSON-formatted logs for log aggregation
+  - Contextual logging with request tracing
+  - Error categorization and detailed error logging
+  - Multiple output handlers (console, file)
+  - Configurable log levels and formats
+
+### 6. **FastAPI Integration**
+- **Complete REST API** (`app.py`):
+  - Text-to-SQL endpoint with model override support
+  - Detailed SQL generation endpoint with processing info
+  - Raw SQL response processing endpoint
+  - Comprehensive health check endpoints
+  - Service information and model listing endpoints
+  - Full monitoring dashboard endpoints
+  - Metrics export endpoints (JSON, Prometheus formats)
+
+## Technical Implementation Details
+
+### API Endpoints
+
+#### Primary Endpoints
+- `POST /api/ollama-text2sql` - Basic SQL generation from natural language
+- `POST /api/ollama-text2sql/detailed` - SQL generation with detailed processing information
+- `POST /api/sql/process` - Process raw SQL responses for testing/debugging
+
+#### Health & Monitoring Endpoints
+- `GET /api/health` - Basic health check
+- `GET /api/health/detailed` - Comprehensive health status
+- `GET /api/health/metrics` - Health metrics and statistics
+- `GET /api/health/history` - Recent health check history
+- `GET /api/service-info` - Service configuration and status
+- `GET /api/models` - Available models from Ollama
+
+#### Monitoring & Analytics Endpoints
+- `GET /api/monitoring/dashboard` - Full monitoring dashboard data
+- `GET /api/monitoring/real-time` - Real-time metrics
+- `GET /api/monitoring/health-status` - Health status for monitoring systems
+- `GET /api/monitoring/performance-trends` - Performance trends (configurable period)
+- `GET /api/monitoring/report/{report_type}` - Comprehensive reports
+- `GET /api/metrics/summary` - Metrics summary
+- `GET /api/metrics/export?format=json|prometheus` - Metrics export
+- `GET /api/metrics/time-series/{metric_name}` - Time series data
+- `GET /api/metrics/requests` - Request history
+
+### Configuration
+Environment variables for flexibility:
+- `OLLAMA_BASE_URL` - Ollama server URL (default: http://localhost:11434)
+- `OLLAMA_MODEL` - Primary model (default: codellama:7b-instruct)
+- `OLLAMA_FALLBACK_MODEL` - Fallback model (default: llama3.2:latest)
+- `OLLAMA_TIMEOUT` - Request timeout in seconds (default: 90)
+- `LOG_LEVEL` - Logging level (default: INFO)
+- `LOG_FORMAT` - Log format: json or text (default: json)
+- `LOG_FILE` - Optional log file path
+- `METRICS_RETENTION_HOURS` - Metrics retention period (default: 24)
+- `MAX_POINTS_PER_METRIC` - Maximum metrics data points (default: 10000)
+
+### SQL Processing Pipeline
+
+1. **Input Processing**: Validate and prepare raw response
+2. **Markdown Removal**: Strip code blocks and formatting
+3. **Ollama Pattern Cleaning**: Remove model-specific response patterns
+4. **Whitespace Cleanup**: Normalize formatting and spacing
+5. **Validation & Extraction**: Validate SQL keywords and extract core expression
+6. **Metadata Generation**: Create processing metadata and complexity estimation
+
+### Error Handling Strategy
+
+**Error Categories:**
+- `TIMEOUT_ERROR` - Request exceeded timeout
+- `CONNECTION_ERROR` - Cannot connect to Ollama
+- `MODEL_ERROR` - Model unavailable or generation failed
+- `VALIDATION_ERROR` - Invalid input
+- `CONFIGURATION_ERROR` - Configuration issues
+- `UNKNOWN_ERROR` - Other errors
+
+**Fallback Mechanism:**
+1. Try primary model
+2. On failure, automatically attempt fallback model
+3. Return error only if both fail
+
+## Files Added
+
+### Core Service Files
+- `app.py` - FastAPI application with all endpoints
+- `config.py` - Configuration management
+- `ollama_client.py` - Ollama integration client
+- `ollama_service.py` - Service layer
+
+### Processing & Validation
+- `sql_processor.py` - Advanced SQL processing and cleaning
+- `model_manager.py` - Model selection and validation
+
+### Monitoring & Observability
+- `health_monitor.py` - Health checking and monitoring
+- `metrics_collector.py` - Metrics collection and analytics
+- `monitoring_dashboard.py` - Dashboard data aggregation
+- `logging_config.py` - Structured logging configuration
+
+### Documentation & Configuration
+- `requirements.txt` - Python dependencies
+- `README.md` - Quick start guide
+- `OLLAMA_NLP_BACKEND_GUIDE.md` - Comprehensive guide
+- `.env.example` - Environment configuration template
+
+## Dependencies
+
+```
+fastapi - Web framework
+uvicorn - ASGI server
+python-dotenv - Environment configuration
+ollama - Ollama Python client
+psutil - System resource monitoring
+pytest - Testing framework
+pytest-asyncio - Async testing
+pytest-cov - Coverage reporting
+```
+
+## Testing
+
+The implementation includes comprehensive testing capabilities:
+- Async operation support with pytest-asyncio
+- Mock health checks and model availability
+- Edge case handling (empty prompts, malformed responses)
+- Error categorization and fallback testing
+- Metrics collection validation
+- Performance benchmarking support
+
+## Deployment Considerations
+
+### Prerequisites
+1. Ollama server running (default: localhost:11434)
+2. At least one model pulled (recommended: codellama:7b-instruct)
+3. Python 3.8+
+
+### Quick Start
+```bash
+cd packages/nlp-backend
+pip install -r requirements.txt
+uvicorn app:app --reload --port 3002
+```
+
+### Production Deployment
+- Use production ASGI server (Gunicorn + Uvicorn)
+- Configure logging to file
+- Set up monitoring/alerting
+- Use environment variables for configuration
+- Implement load balancing if needed
+
+## Performance Characteristics
+
+- **Typical Response Time**: 3-15 seconds (model-dependent)
+- **Fallback Logic**: <1 second additional delay
+- **Health Check Overhead**: <10ms
+- **Metrics Collection**: Lock-based thread-safe implementation
+- **Memory Usage**: ~50-200MB base + model size
+
+## Security Considerations
+
+1. **SQL Injection Prevention**:
+   - Basic pattern detection for dangerous keywords
+   - User disclaimer for custom expressions
+   - SQL validation before output
+
+2. **Local Processing**:
+   - No external API calls
+   - Data remains on user's infrastructure
+   - No cloud dependencies
+
+3. **Input Validation**:
+   - Prompt length checking
+   - Empty input rejection
+   - Response validation
+
+## Backward Compatibility
+- Service is new, no breaking changes to existing code
+- Can be optionally enabled/disabled in UI
+- Falls back to manual SQL entry if service unavailable
+
+## Documentation Provided
+- Comprehensive setup guide with platform-specific instructions
+- API documentation for all endpoints
+- Configuration reference
+- Usage examples
+- Troubleshooting section
+
+## Demonstration
+
+### Video Walkthrough
+A comprehensive demonstration of the Ollama NLP Backend for Custom Computed Fields:
+
+<video width="100%" controls>
+  <source src="./packages/nlp-backend/NLP-Based-Compute-Field.mp4" type="video/mp4">
+  Your browser does not support the video tag. <a href="./packages/nlp-backend/NLP-Based-Compute-Field.mp4">Download video</a>
+</video>
+
+**[📹 Download: NLP-Based Compute Field](./packages/nlp-backend/NLP-Based-Compute-Field.mp4)** *(Right-click → Save link as... to download)*
+
+The video covers:
+- ✅ Service startup and initialization
+- ✅ Health check system in action
+- ✅ Natural language to SQL conversion examples
+- ✅ Fallback model mechanism
+- ✅ Error handling and recovery
+- ✅ Monitoring dashboard overview
+- ✅ Metrics collection and analysis
+- ✅ Real-world usage scenarios
+
+### Example Conversions
+
+**Example 1: Conditional Logic**
+```
+Prompt: "If revenue > 1000 then High else Low"
+Generated SQL: CASE WHEN revenue > 1000 THEN 'High' ELSE 'Low' END
+```
+
+**Example 2: Mathematical Operation**
+```
+Prompt: "Calculate 10% of price"
+Generated SQL: price * 0.1
+```
+
+**Example 3: Complex Condition**
+```
+Prompt: "If status is active then Premium else Basic"
+Generated SQL: CASE WHEN status = 'active' THEN 'Premium' ELSE 'Basic' END
+```
+
+**Example 4: Multi-field Logic**
+```
+Prompt: "If revenue > cost then profit else loss"
+Generated SQL: CASE WHEN revenue > cost THEN 'profit' ELSE 'loss' END
+```
+
+## Related Issues
+- Enables custom computed fields feature in Graphic Walker
+- Provides foundation for AI-powered UI enhancements
+
+## Testing Checklist
+- ✅ Service startup and initialization
+- ✅ Health check endpoints
+- ✅ Basic SQL generation
+- ✅ Model fallback on failure
+- ✅ Error categorization
+- ✅ Metrics collection
+- ✅ Request history tracking
+- ✅ Monitoring dashboard data
+- ✅ Different model types
+- ✅ Edge cases (empty input, timeouts, etc.)
+
+## Notes for Reviewers
+
+1. **SQL Processing**: The regex-based cleaning pipeline is designed to handle various Ollama model output formats
+2. **Async Implementation**: Uses thread pool for sync Ollama client operations
+3. **Health Monitoring**: Comprehensive but can be extended for specific infrastructure needs
+4. **Metrics**: Thread-safe collection with configurable retention
+5. **Logging**: Structured JSON format for easy log aggregation