####################################
# LogSentinelAI Configuration File #
####################################

# =============================================================================
# Telegram Alert Configuration
# =============================================================================
# Enable/Disable Telegram notifications (true/false)
# When false, all Telegram alert processing is skipped for better performance
TELEGRAM_ENABLED=true

# Telegram Alert Level - Minimum severity level to trigger alerts
# Choose from: CRITICAL, HIGH, MEDIUM, LOW, INFO
# Only events with this severity level or higher will trigger Telegram alerts
# Example: If set to HIGH, only CRITICAL and HIGH events will send alerts
# Example: If set to MEDIUM, CRITICAL, HIGH, and MEDIUM events will send alerts
TELEGRAM_ALERT_LEVEL=CRITICAL

# Telegram Bot Token (required for alerting to Telegram group)
# Get from @BotFather after creating your bot
TELEGRAM_TOKEN=YOUR_TELEGRAM_BOT_TOKEN_HERE

# Telegram Chat ID (group or user)
# Use @userinfobot or similar to get your group chat ID (negative number for groups)
TELEGRAM_CHAT_ID=YOUR_TELEGRAM_CHAT_ID_HERE

# =============================================================================
# Logging Configuration
# =============================================================================

# LOG_LEVEL: Set logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
# Example: LOG_LEVEL=INFO
LOG_LEVEL=INFO

# LOG_FILE: Path to log file (leave empty for console only)
# Example: LOG_FILE=logsentinelai.log
LOG_FILE=/var/log/logsentinelai.log

# ===============================================================================
# API Keys
# =============================================================================
# OpenAI API Key (required if using OpenAI provider)
OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HERE

# Gemini API Key (required if using Gemini provider)
GEMINI_API_KEY=AIzaSyCtQ-SyLUn7dyorwD_Nzq8dWLQjqV2EKb

# =============================================================================
# LLM Configuration
# =============================================================================
# LLM Provider - Choose from "ollama", "vllm", "openai", "gemini"
LLM_PROVIDER=ollama
#LLM_PROVIDER=vllm
#LLM_PROVIDER=gemini
#LLM_PROVIDER=openai

# LLM Model - Ollama
LLM_MODEL_OLLAMA=qwen3:8b
#LLM_MODEL_OLLAMA=gemma3:4b
#LLM_MODEL_OLLAMA=qwen2.5:1.5b
#LLM_MODEL_OLLAMA=qwen3:1.7b

# LLM Model - vLLM
LLM_MODEL_VLLM=Qwen/Qwen2.5-1.5B-Instruct

# LLM Model - Gemini
LLM_MODEL_GEMINI=gemini-1.5-pro
#LLM_MODEL_GEMINI=gemini-2.5-flash-lite

# LLM Model - OpenAI
#LLM_MODEL_OPENAI=gpt-4o-mini
LLM_MODEL_OPENAI=gpt-4.1-nano

# Ollama API Host Configuration (default: http://127.0.0.1:11434/v1)
# (OpenAI API compatibility)
LLM_API_HOST_OLLAMA=http://192.168.140.198:11434/v1

# vLLM API host (default: http://127.0.0.1:5000/v1)
# (OpenAI API compatibility)
LLM_API_HOST_VLLM=http://127.0.0.1:5000/v1

# OpenAI API host (default: https://api.openai.com/v1)
LLM_API_HOST_OPENAI=https://api.openai.com/v1

# Gemini API host
# (OpenAI API compatibility)
LLM_API_HOST_GEMINI=https://generativelanguage.googleapis.com/v1beta/openai/

# LLM Generation Parameters
# Temperature: Controls randomness (0.0 = deterministic, 1.0 = very random)
# Recommended: 0.0-0.1 for log analysis (consistency), 0.7-0.9 for creative tasks
LLM_TEMPERATURE=0.1

# Top-p: Controls diversity via nucleus sampling (0.1-1.0)
# Lower values = more focused, Higher values = more diverse
LLM_TOP_P=0.3

# Max Tokens: Maximum number of tokens to generate in response
# Controls response length and prevents infinite generation
# Recommended: 1024-4096 for log analysis depending on complexity
LLM_MAX_TOKENS=4096

# Show Prompt: Display the full prompt sent to LLM before processing (true/false)
# Set to true to see the complete prompt for debugging and transparency
# Useful for debugging prompt issues and understanding what's sent to the LLM
LLM_SHOW_PROMPT=true

# =============================================================================
# Analysis Configuration
# =============================================================================
# Response Language - Choose from "english", "korean", etc..
RESPONSE_LANGUAGE=english

# Analysis Mode - Choose from "batch", "realtime"
# batch: Analyze complete log files (existing functionality)
# realtime: Monitor and analyze new log entries as they are written
ANALYSIS_MODE=batch

# =============================================================================
# Log File Paths
# =============================================================================
# Default log file paths used when --log-path is not specified
LOG_PATH_HTTPD_ACCESS=/workspace/LogSentinelAI/sample-logs/access-10k.log
LOG_PATH_HTTPD_SERVER=/workspace/LogSentinelAI/sample-logs/apache-10k.log
LOG_PATH_LINUX_SYSTEM=/workspace/LogSentinelAI/sample-logs/linux-2k.log
LOG_PATH_GENERAL_LOG=/var/log/secure

# =============================================================================
# Chunk Size Configuration (entries per chunk)
# =============================================================================
# CHUNK_SIZE: Number of log entries sent to LLM for analysis in a single request
# - Controls analysis quality vs performance balance
# - Larger values: Better context understanding, slower processing
# - Smaller values: Faster processing, may miss complex patterns
# - Recommended: 10-50 depending on log complexity and LLM capacity
# - These values are used for both batch and real-time modes
# CLI Override: Use --chunk-size argument to override these values
# Example: python analysis-linux-system-log.py --chunk-size 20
CHUNK_SIZE_HTTPD_ACCESS=10
CHUNK_SIZE_HTTPD_SERVER=10
CHUNK_SIZE_LINUX_SYSTEM=10
CHUNK_SIZE_GENERAL_LOG=10

# =============================================================================
# Elasticsearch Configuration
# =============================================================================
ELASTICSEARCH_HOST=http://localhost:9200
ELASTICSEARCH_USER=elastic
ELASTICSEARCH_PASSWORD=password
ELASTICSEARCH_INDEX=logsentinelai-analysis

# =============================================================================
# Real-time Monitoring Configuration
# =============================================================================
# Remote Log Access Mode - Choose from "local", "ssh"
# local: Monitor local log files (default)
# ssh: Monitor remote log files via SSH connection
REMOTE_LOG_MODE=local

# SSH Remote Server Configuration (only used when REMOTE_LOG_MODE=ssh)
# SSH connection details for remote log monitoring
REMOTE_SSH_HOST=
REMOTE_SSH_PORT=22
REMOTE_SSH_USER=
# SSH Key Authentication (recommended for security)
REMOTE_SSH_KEY_PATH=
# Password Authentication (less secure, use only if SSH key is not available)
REMOTE_SSH_PASSWORD=
# SSH Connection Timeout (seconds)
REMOTE_SSH_TIMEOUT=10

# Polling interval for checking new log entries (seconds)
# - How often to check log files for new content
# - Lower values: More responsive, higher CPU usage
# - Higher values: Less responsive, lower CPU usage
REALTIME_POLLING_INTERVAL=5

# Maximum number of new lines to process at once (I/O efficiency control)
# - Limits memory usage and prevents system overload
# - Controls how many lines are read from file system per polling cycle
# - IMPORTANT: This is different from CHUNK_SIZE (which controls LLM analysis batching)
# - If 1000 new lines appear, this setting reads only 50 lines per cycle
# - Remaining lines are processed in subsequent polling cycles
# - Recommended: 20-100 depending on system resources and log volume
REALTIME_MAX_LINES_PER_BATCH=50

# Buffer time to wait for complete log lines (seconds)
# - Prevents processing incomplete log lines that are still being written
# - When new lines are detected, waits this many seconds before processing
# - Ensures log entries are completely written to disk before analysis
# - Higher values: More safety against incomplete lines, slower responsiveness
# - Lower values: Faster processing, risk of reading partial log entries
# - Recommended: 1-5 seconds depending on log writing frequency
REALTIME_BUFFER_TIME=2

# Real-time processing mode - Choose from "full", "sampling"
# full: Process all accumulated logs sequentially (default)
# sampling: Only process the most recent chunk_size logs, discard older ones
# CLI Override: Use --processing-mode argument to override this setting
# Example: python analysis-linux-system-log.py --mode realtime --processing-mode sampling
REALTIME_PROCESSING_MODE=full

# Chunk pending timeout (seconds) for forcing pending logs to be processed
# - Maximum time to wait for chunk_size logs to accumulate before processing
# - If pending logs exist but chunk_size is not reached within this time, process anyway
# - Prevents pending logs from waiting indefinitely in low-volume environments
# - Set to 0 to disable timeout (wait indefinitely for full chunks)
# - Recommended: 600-3600 seconds (10-60 minutes) depending on log volume and analysis urgency
REALTIME_CHUNK_PENDING_TIMEOUT=600

# Auto-sampling threshold (applies to full mode)
# Automatically switch to sampling if accumulated lines exceed this number
# - When pending lines exceed this threshold, automatically switch to sampling mode
# - Prevents memory buildup in high-volume log environments
# - Works with CHUNK_SIZE: if CHUNK_SIZE=10 and threshold=100, keeps latest 10 lines
# CLI Override: Use --sampling-threshold argument to override this setting
# Example: python analysis-linux-system-log.py --mode realtime --sampling-threshold 200
REALTIME_SAMPLING_THRESHOLD=100

# FLOW EXPLANATION:
# 1. Every REALTIME_POLLING_INTERVAL seconds, check log file for new lines
# 2. Read up to REALTIME_MAX_LINES_PER_BATCH lines from file system
# 3. Add these lines to pending buffer (internal queue)
# 4. When pending buffer reaches CHUNK_SIZE lines, send to LLM for analysis
# 5. If pending buffer exceeds REALTIME_SAMPLING_THRESHOLD, apply sampling
# 6. If pending logs exist but CHUNK_SIZE not reached within REALTIME_CHUNK_PENDING_TIMEOUT seconds, force process anyway
#
# Example with current settings:
# - Check file every 5 seconds
# - Read max 50 lines per check
# - Analyze when 10 lines accumulated (CHUNK_SIZE)
# - Apply sampling if 100+ lines pending (REALTIME_SAMPLING_THRESHOLD)
# - Force process pending logs after 1800 seconds (30 minutes) timeout (REALTIME_CHUNK_PENDING_TIMEOUT)

# Note: Real-time monitoring will buffer new log lines until CHUNK_SIZE lines
# are accumulated before sending to LLM for analysis. This improves efficiency
# by avoiding single-line analysis calls. However, if logs don't reach CHUNK_SIZE
# within the timeout period, they will be processed anyway to prevent indefinite waiting.

# =============================================================================
# GeoIP Configuration
# =============================================================================

# Enable GeoIP enrichment of source IP addresses with city/location information
# When enabled, source_ips and related fields in analysis results will include city, country, and coordinates (geo_point)
GEOIP_ENABLED=true

# Path to MaxMind GeoLite2-City database file (.mmdb format)
# Download with: python -m logsentinelai.utils.geoip_downloader
# Default: ~/.logsentinelai/GeoLite2-City.mmdb (automatically downloaded if missing)
GEOIP_DATABASE_PATH=~/.logsentinelai/GeoLite2-City.mmdb

# Fallback country name for unknown IP addresses
GEOIP_FALLBACK_COUNTRY=Unknown

# Include private/internal IP addresses in GeoIP processing
# When false, private IPs (192.168.x.x, 10.x.x.x, etc.) are marked as "Private IP"
# When true, private IPs are processed through GeoIP database (usually results in "Unknown")
GEOIP_INCLUDE_PRIVATE_IPS=false

# Size of GeoIP lookup cache (number of IP addresses to cache)
# Higher values reduce database queries but consume more memory
GEOIP_CACHE_SIZE=1000