golang-ai-server

Go-based AI Server with Comprehensive Debug Logging

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Overview

golang-ai-server is a server application written in Go that provides AI processing capabilities with Ollama integration. It supports multi-modal input including text prompts and images, with comprehensive debug logging for development, troubleshooting, and monitoring.

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Features

• Ollama Integration: Direct integration with Ollama API for AI processing
• Multi-modal Input: Supports text prompts and image file processing
• Comprehensive Logging: Structured debug logging with multiple levels and formats
• File Processing: Base64 encoding for images and PDFs
• Performance Monitoring: Request timing and performance metrics
• Development-Friendly: Extensive debugging information for development

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Getting Started

Prerequisites

• Go (version 1.21+ recommended for slog support)
• Ollama running locally on port 11434
• Images or files to process (optional)

Installation

Clone the repository:

git clone https://github.com/bittelc/golang-ai-server.git
cd golang-ai-server

Install dependencies:

go mod tidy

Quick Start

Run with default settings:

go run main.go

Run with debug logging:

LOG_LEVEL=DEBUG go run main.go

Run with JSON logging format:

LOG_LEVEL=INFO LOG_FORMAT=json go run main.go

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Logging System

This application features a comprehensive logging system for debugging, monitoring, and development purposes.

Log Levels

• DEBUG: Detailed debugging information, file operations, HTTP details
• INFO: General application flow and status (default)
• WARN: Warning conditions and incomplete responses
• ERROR: Error conditions and failures

Log Formats

• text: Human-readable text format (default)
• json: Structured JSON format for log aggregation

Environment Variables

# Set log level
export LOG_LEVEL=DEBUG    # DEBUG, INFO, WARN, ERROR

# Set log format  
export LOG_FORMAT=json    # text, json

What Gets Logged

1. Application Lifecycle

   • Startup and shutdown events
   • Configuration loading
   • Total execution time

2. User Input Processing

   • Prompt collection and validation
   • Image path parsing and validation
   • File reading and encoding operations

3. File Operations

   • File opening, reading, and encoding
   • File size tracking and performance
   • Base64 encoding progress

4. HTTP Operations

   • Ollama API requests and responses
   • Request/response timing and size
   • HTTP status codes and headers

5. Error Handling

   • Detailed error context and stack traces
   • Operation failure points
   • Recovery attempts

Logging Demo

Run the interactive logging demonstration:

./demo_logging.sh

Development Tips

View detailed logs during development:

LOG_LEVEL=DEBUG go run main.go 2>&1 | tee app.log

For production-like monitoring:

LOG_LEVEL=INFO LOG_FORMAT=json go run main.go

See LOGGING.md for complete logging documentation.

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Usage

1. Start the application:

   go run main.go
   

2. Enter your prompt when asked

3. Optionally provide image paths (comma-separated, max 5):

   /path/to/image1.jpg, /path/to/image2.png
   

4. View the AI response and processing logs

Example Session

$ LOG_LEVEL=INFO go run main.go
User prompt: Describe this image
Path to images, separated by commas, limit of 5 (optional): test_image.txt
[Processing logs will appear here]
[AI response will appear here]
Completed in 2.5s

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Architecture

Components

• main.go: Application entry point and orchestration
• input/: User input handling and file processing
• ollama/: Ollama API client and request handling
• logger/: Centralized logging utilities and configuration

File Structure

golang-ai-server/
├── main.go              # Main application
├── input/
│   └── input.go         # User input and file processing
├── ollama/
│   └── server.go        # Ollama API client
├── logger/
│   └── logger.go        # Logging utilities
├── LOGGING.md           # Logging documentation
├── demo_logging.sh      # Logging demonstration
└── test_image.txt       # Test file for logging demo

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Development

Adding New Logging

When adding new functionality, use the logging utilities:

// Log processing steps
logger.LogProcessingStep("operation_name", map[string]interface{}{
    "param1": value1,
    "param2": value2,
})

// Log errors with context
logger.LogError("operation_name", err, map[string]interface{}{
    "context1": value1,
    "context2": value2,
})

// Log file operations
logger.LogFileOperation("read_file", filePath, fileSize)