.gbot Bot Configuration

The .gbot package serves as the configuration center for your bot, containing the settings that define how the bot behaves, which AI models it uses, and how it interacts with users. This chapter explains the configuration system and guides you through the available options.

Understanding Bot Configuration

Every bot in General Bots requires configuration to operate effectively. The .gbot folder within your bot package holds these settings, primarily through a config.csv file that uses simple key-value pairs. This approach makes configuration accessible to anyone comfortable with spreadsheet applications while remaining powerful enough for complex deployments.

The configuration system influences several aspects of bot behavior. Bot identity settings control how the bot presents itself to users. LLM configuration determines which language model powers the bot’s intelligence and how it generates responses. Context management settings affect how the bot maintains conversation history and retrieves relevant information. Integration parameters connect the bot to external services and APIs.

The config.csv File

Configuration lives in a straightforward CSV format with two columns: key and value. This design choice prioritizes accessibility—you can edit configuration in any text editor or spreadsheet application without learning complex syntax. Each row represents a single setting, making it easy to scan and modify.

The file supports various data types implicitly. Text values are stored as-is, numbers are parsed when needed, and boolean values typically use “true” and “false” strings. The system handles type conversion automatically when reading configuration, so you rarely need to worry about explicit typing.

Bot Identity Configuration

Identity settings establish how your bot presents itself during conversations. The bot_name parameter provides the display name users see when interacting with the bot. A descriptive bot_description helps users understand the bot’s purpose and capabilities. Version tracking through the version parameter supports deployment management and debugging.

These identity settings matter because they shape user expectations. A bot named “Legal Document Assistant” with an appropriate description sets different expectations than a generic “Helper Bot.” Clear identity configuration improves user experience by establishing context before conversations begin.

Language Model Settings

LLM configuration represents perhaps the most important settings in your bot. The llm_provider parameter specifies which AI service powers your bot, supporting options like OpenAI, Azure OpenAI, or local model servers. The llm_model parameter identifies the specific model to use, such as GPT-4 or Claude 3.

Response characteristics are controlled through several parameters. The temperature setting affects response creativity, with lower values producing more focused and deterministic outputs while higher values allow more varied and creative responses. The max_tokens parameter limits response length, preventing runaway generation and managing costs for cloud-based providers.

The system_prompt parameter provides instructions that shape the bot’s personality and behavior throughout conversations. This prompt is prepended to every interaction, giving the model consistent guidance about how to respond, what tone to use, and what boundaries to respect.

Context Management

Context settings control how the bot maintains awareness of conversation history and relevant information. The context_window parameter determines how many previous messages remain visible to the model during each interaction. Larger windows provide better continuity but consume more tokens.

The context_provider setting influences how context is assembled and presented to the model. Different providers may apply various strategies for selecting and formatting context, optimizing for different use cases.

Memory functionality, controlled by the memory_enabled setting, allows bots to retain information across sessions. When enabled, bots can remember user preferences, previous interactions, and other persistent data that improves personalization.

Configuration Loading and Precedence

The system assembles configuration from multiple sources, applying them in a specific order that allows flexible overrides. Default values provide baseline behavior when no explicit configuration exists. Settings in your .gbot/config.csv file override these defaults for your specific bot.

Environment variables can override config.csv settings, useful for deployment scenarios where configuration varies between environments. Database configuration provides another override layer, supporting runtime configuration changes that persist across restarts. Finally, runtime API calls can temporarily adjust settings without permanent changes.

This precedence system enables sophisticated deployment patterns. Development environments might use local configuration files while production deployments pull settings from environment variables or databases. The same bot package can behave differently across environments without modification.

Dynamic Configuration with Bot Memory

Beyond static configuration, bots can store and retrieve dynamic settings using bot memory. The SET BOT MEMORY keyword stores values that persist across all sessions, effectively creating runtime configuration that can be modified through bot scripts.

This capability supports scenarios where configuration needs to adapt based on usage patterns, administrative decisions, or external inputs. A bot might store preferred response styles, accumulated statistics, or cached data that influences its behavior.

Best Practices

Effective configuration follows several principles. Keep identity settings clear and accurate—users trust bots more when their purpose is evident. Choose LLM settings that balance capability with cost and latency requirements. Set appropriate context windows that provide continuity without excessive token consumption.

Document non-obvious configuration choices, either in comments within config.csv or in accompanying documentation. This practice helps future maintainers understand why settings were chosen and whether they should be adjusted.

Test configuration changes in development environments before deploying to production. Some settings interact in non-obvious ways, and testing catches issues before they affect users.

Summary

The .gbot configuration system provides comprehensive control over bot behavior through accessible CSV files augmented by environment variables, database settings, and runtime adjustments. Understanding these configuration options and their precedence helps you build bots that behave predictably across different deployment scenarios while remaining adaptable to changing requirements.