Add Local Ollama NLP Backend for Natural-Language Computed Fields

[kkhatke]

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

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

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:

📹 Download: NLP-Based Compute Field (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

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[kkhatke]

Hi! This PR is ready for review.
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Please approve the checks whenever convenient.
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