Configuration Management

Configuration in General Bots is designed to be simple and transparent. Each bot uses a config.csv file for settings, with additional environment variables for system-level configuration.

The config.csv File

Located in your bot’s .gbot package, this file controls all bot-specific settings using simple name-value pairs.

File Format

name,value
setting_name,setting_value
another_setting,another_value

Empty rows are used for visual grouping
No quotes needed for string values
Case-sensitive names
Comments not supported (keep it simple)

Core Configuration Sections

Server Configuration

server-host,0.0.0.0
server-port,8080
sites-root,/tmp

Name	Description	Default	Example
`server-host`	Bind address for the web server	`0.0.0.0`	`0.0.0.0`
`server-port`	Port for the web interface	`8080`	`8080`
`sites-root`	Directory for generated sites	`/tmp`	`/tmp`

LLM Configuration - Overview

For detailed LLM configuration, see the tables below. The basic settings are:

llm-key,none
llm-url,http://localhost:8081
llm-model,../../../../data/llm/DeepSeek-R3-Distill-Qwen-1.5B-Q3_K_M.gguf

Core LLM Settings

Name	Description	Default	Example
`llm-key`	API key for LLM service	`none`	`gsk-...` for Groq
`llm-url`	LLM service endpoint	`http://localhost:8081`	`https://api.groq.com/openai/v1`
`llm-model`	Model path or name	Required	`mixtral-8x7b-32768`

Model Path Options

Local GGUF: ../../../../data/llm/model.gguf
Absolute path: /opt/models/model.gguf
Cloud model name: mixtral-8x7b-32768 (for Groq)

Supported Formats

GGUF: Quantized models (Q3_K_M, Q4_K_M, Q5_K_M, Q8_0)
API Models: Any Groq or OpenAI-compatible model

LLM Cache Settings

llm-cache,false
llm-cache-ttl,3600
llm-cache-semantic,true
llm-cache-threshold,0.95

Name	Description	Default	Type
`llm-cache`	Enable response caching	`false`	Boolean
`llm-cache-ttl`	Cache time-to-live in seconds	`3600`	Number
`llm-cache-semantic`	Use semantic similarity	`true`	Boolean
`llm-cache-threshold`	Similarity threshold for cache hits	`0.95`	Float

Cache Strategy Tips:

Enable for production to reduce API costs
Semantic cache finds similar (not just identical) queries
Lower threshold (0.90) = more hits but less precision
Higher threshold (0.98) = fewer hits but exact matches

Context Management

episodic-memory-threshold,4
episodic-memory-history,2

Name	Description	Default	Range
`episodic-memory-threshold`	Messages before compaction	`4`	1-10
`episodic-memory-history`	Messages to keep in history	Not set	1-20

Embedding Configuration

embedding-url,http://localhost:8082
embedding-model,../../../../data/llm/bge-small-en-v1.5-f32.gguf

Name	Description	Default	Type
`embedding-url`	Embedding service endpoint	`http://localhost:8082`	URL
`embedding-model`	Path to embedding model	Required for KB	Path

LLM Server Settings (When Self-Hosting)

llm-server,true
llm-server-path,botserver-stack/bin/llm/build/bin
llm-server-host,0.0.0.0
llm-server-port,8081
llm-server-gpu-layers,0
llm-server-n-moe,0
llm-server-ctx-size,4096
llm-server-n-predict,1024
llm-server-parallel,6
llm-server-cont-batching,true
llm-server-mlock,false
llm-server-no-mmap,false

Performance Parameters

Parameter	Description	Default	Impact
`llm-server-gpu-layers`	Layers to offload to GPU	`0`	0=CPU only, higher=more GPU usage
`llm-server-n-moe`	MoE experts count	`0`	Enables 120B+ models on consumer GPUs
`llm-server-ctx-size`	Context window (tokens)	`4096`	More context = more memory
`llm-server-n-predict`	Max output tokens	`1024`	Limits response length
`llm-server-parallel`	Concurrent requests	`6`	Higher = more throughput
`llm-server-cont-batching`	Continuous batching	`true`	Better multi-user performance
`llm-server-mlock`	Lock model in RAM	`false`	Prevents swapping to disk
`llm-server-no-mmap`	Disable memory mapping	`false`	Uses more RAM but may be faster

Hardware-Specific Settings

RTX 3090 (24GB VRAM)

Set llm-server-gpu-layers to 35-45 for 7B-32B models
Enable llm-server-n-moe 2-4 for 120B+ models
Can run DeepSeek-V3 with proper MoE settings

RTX 4070/Ti (12-16GB)

Set llm-server-gpu-layers to 25-30 for 7B-14B models
Keep llm-server-ctx-size at 4096 to save VRAM

CPU-Only Setup

Keep llm-server-gpu-layers at 0
Enable llm-server-mlock to prevent swapping
Set llm-server-parallel to CPU cores -2

Email Configuration

email-from,from@domain.com
email-server,mail.domain.com
email-port,587
email-user,user@domain.com
email-pass,password

All email parameters are required if you want to send emails from your bot.

Custom Database (Optional)

custom-server,localhost
custom-port,5432
custom-database,mycustomdb
custom-username,dbuser
custom-password,dbpass

Configuration Examples

Minimal Configuration

name,value
server-port,8080
llm-url,http://localhost:8081
llm-model,../../../../data/llm/model.gguf

Production Configuration (Groq Cloud)

name,value
,
server-host,0.0.0.0
server-port,443
sites-root,/var/www/sites
,
# Groq is 10x faster than traditional cloud providers
llm-key,gsk-your-groq-api-key
llm-url,https://api.groq.com/openai/v1
llm-model,mixtral-8x7b-32768
,
llm-cache,true
llm-cache-ttl,7200
llm-cache-semantic,true
llm-cache-threshold,0.95
,
episodic-memory-threshold,6
,
email-from,bot@company.com
email-server,smtp.company.com
email-port,587
email-user,bot@company.com
email-pass,secure-password

Local Development (Self-Hosted)

name,value
,
server-port,3000
,
# Run your own LLM server
llm-server,true
llm-server-gpu-layers,35
llm-server-ctx-size,8192
llm-server-n-predict,2048
llm-model,../../../../data/llm/DeepSeek-R3-Distill-Qwen-7B-Q4_K_M.gguf
,
# Disable cache for development
llm-cache,false
episodic-memory-threshold,2

Configuration Priority

Settings are applied in this order (later overrides earlier):

Default values in code
config.csv settings

Best Practices

Keep it Simple: Only configure what you need to change
Use Groups: Empty rows make the file readable
Test Locally: Verify settings before production
Secure Secrets: Use environment variables for passwords in production
Document Changes: Comment significant changes in version control

Validation

The system validates configuration on startup:

Missing required values cause clear error messages
Invalid URLs or paths are detected early
Port conflicts are reported
Model file existence is verified

Hot Reload

Some settings support hot reload without restart:

Cache settings
Context parameters
Email configuration

Others require restart:

Server ports
LLM model changes
Database connections

Troubleshooting

Common Issues

Port Already in Use

Change server-port to an available port
Check for other services on the same port

Model Not Found

Verify the path in llm-model is correct
Ensure the GGUF file exists
Use absolute paths if relative paths fail

LLM Server Won’t Start

Check llm-server-gpu-layers doesn’t exceed your GPU capability
Reduce llm-server-ctx-size if out of memory
Set llm-server-gpu-layers to 0 for CPU-only
Verify model file exists at the specified path
Check available VRAM with nvidia-smi (if using GPU)

Cache Not Working

Ensure llm-cache is set to true
Check llm-cache-threshold isn’t too high (0.95 is usually good)
Verify Valkey/Redis is running

Quick Model Recommendations

Best Models by Hardware

24GB+ VRAM (RTX 3090, 4090)

DeepSeek-V3 (with MoE enabled)
Qwen2.5-32B-Instruct-Q4_K_M
DeepSeek-R3-Distill-Qwen-14B (runs fast with room to spare)

12-16GB VRAM (RTX 4070, 4070Ti)

DeepSeek-R3-Distill-Llama-8B
Qwen2.5-14B-Q4_K_M
Mistral-7B-Instruct-Q5_K_M

8GB VRAM or CPU-Only

DeepSeek-R3-Distill-Qwen-1.5B
Phi-3-mini-4k-instruct
Qwen2.5-3B-Instruct-Q5_K_M

Cloud API (Fastest)

Groq: mixtral-8x7b-32768
Groq: llama-3.1-70b-versatile

General Bots configuration is intentionally simple - a CSV file with name-value pairs. No complex YAML, no nested JSON, just straightforward settings that anyone can edit. Start with minimal configuration and add settings as needed.

For LLM configuration, the key decision is local vs cloud:

Local: Full control, no recurring costs, complete privacy
Cloud (Groq): 10x faster inference, pay-per-use, no hardware needed

General Bots Documentation