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botserver/templates/default.gbai/default.gbot/config.csv

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Add AWS SDK integration and update bot configuration management - Introduced AWS SDK dependencies for S3 and CSV handling. - Implemented logic to check and update the default bot configuration in S3 after component installation. - Added a new configuration CSV template for bot settings. - Refactored package manager to register cache component with updated download URL and binary name. - Updated README and Cargo files to reflect new dependencies and configuration options.
2025-10-26 14:15:43 -03:00
name,value
feat(llm): pass model configuration to LLM generation and streaming Include model parameter in LLM provider calls across automation, bot, and keyword modules to ensure correct model selection based on configuration. This improves flexibility and consistency in LLM usage.
2025-11-12 08:19:21 -03:00
,
refactor(config): remove unused S3 bucket configuration and setup logic Eliminates the `s3_bucket` field from `AppConfig` and deletes related initialization code in `main.rs`. This simplifies configuration management since S3 bucket handling is no longer required or used in the application.
2025-10-29 09:54:39 -03:00
server_host,0.0.0.0
server_port,8080
sites_root,/tmp
feat(llm): pass model configuration to LLM generation and streaming Include model parameter in LLM provider calls across automation, bot, and keyword modules to ensure correct model selection based on configuration. This improves flexibility and consistency in LLM usage.
2025-11-12 08:19:21 -03:00
,
feat: refactor database connection handling and add LLM component - Replace direct database connection establishment with shared `establish_pg_connection` utility - Add "llm" to required components list in bootstrap manager - Lower default RUST_LOG level from debug to info in VSCode config - Clean up imports and connection error messages - Remove hardcoded database URL strings in favor of centralized connection handling The changes improve code maintainability by centralizing database connection logic and adding support for the new LLM component in the bootstrap process.
2025-11-01 07:20:04 -03:00
llm-key,none
refactor(config): replace raw SQL with Diesel query for bot config Updated `ConfigManager::get_config` to use Diesel query builder instead of raw SQL for improved safety and maintainability. Adjusted parameter naming and integrated schema references. Also refactored `ensure_llama_servers_running` to fetch configuration from the database using `AppState` and `ConfigManager`. Removed unused imports in bootstrap module.
2025-11-01 11:48:46 -03:00
llm-url,http://localhost:8081
llm-model,../../../../data/llm/DeepSeek-R1-Distill-Qwen-1.5B-Q3_K_M.gguf
feat(llm): pass model configuration to LLM generation and streaming Include model parameter in LLM provider calls across automation, bot, and keyword modules to ensure correct model selection based on configuration. This improves flexibility and consistency in LLM usage.
2025-11-12 08:19:21 -03:00
,
- New stuff, 6.1.
2025-11-21 23:23:53 -03:00
llm-cache,false
llm-cache-ttl,3600
llm-cache-semantic,true
llm-cache-threshold,0.95
,
feat(email): implement email read tracking with pixel support - Add email-read-pixel config parameter to enable/disable tracking - Implement tracking pixel injection in HTML emails - Add sent_email_tracking table with migration - Create 4 new API endpoints: - GET /api/email/tracking/pixel/{id} - serve pixel & record read - GET /api/email/tracking/status/{id} - get email read status - GET /api/email/tracking/list - list all tracked emails - GET /api/email/tracking/stats - get aggregate statistics - Store tracking data: read_at, read_count, IP, user_agent - Integrate with send_email() to auto-inject pixel when enabled
2025-12-04 18:15:09 -03:00
episodic-memory-threshold,4
feat(llm): pass model configuration to LLM generation and streaming Include model parameter in LLM provider calls across automation, bot, and keyword modules to ensure correct model selection based on configuration. This improves flexibility and consistency in LLM usage.
2025-11-12 08:19:21 -03:00
,
feat: refactor auth and models, update LLM fallback strategy - Simplified auth module by removing unused imports and code - Cleaned up shared models by removing unused structs (Organization, User, Bot, etc.) - Updated add-req.sh to comment out unused directories - Modified LLM fallback strategy in README with additional notes about model behaviors The changes focus on removing unused code and improving documentation while maintaining existing functionality. The auth module was significantly reduced by removing redundant code, and similar cleanup was applied to shared models. The build script was adjusted to reflect currently used directories.
2025-11-04 23:11:33 -03:00
mcp-server,false
feat(llm): pass model configuration to LLM generation and streaming Include model parameter in LLM provider calls across automation, bot, and keyword modules to ensure correct model selection based on configuration. This improves flexibility and consistency in LLM usage.
2025-11-12 08:19:21 -03:00
,
Add AWS SDK integration and update bot configuration management - Introduced AWS SDK dependencies for S3 and CSV handling. - Implemented logic to check and update the default bot configuration in S3 after component installation. - Added a new configuration CSV template for bot settings. - Refactored package manager to register cache component with updated download URL and binary name. - Updated README and Cargo files to reflect new dependencies and configuration options.
2025-10-26 14:15:43 -03:00
embedding-url,http://localhost:8082
refactor(config): replace raw SQL with Diesel query for bot config Updated `ConfigManager::get_config` to use Diesel query builder instead of raw SQL for improved safety and maintainability. Adjusted parameter naming and integrated schema references. Also refactored `ensure_llama_servers_running` to fetch configuration from the database using `AppState` and `ConfigManager`. Removed unused imports in bootstrap module.
2025-11-01 11:48:46 -03:00
embedding-model,../../../../data/llm/bge-small-en-v1.5-f32.gguf
feat(llm): pass model configuration to LLM generation and streaming Include model parameter in LLM provider calls across automation, bot, and keyword modules to ensure correct model selection based on configuration. This improves flexibility and consistency in LLM usage.
2025-11-12 08:19:21 -03:00
,
Add AWS SDK integration and update bot configuration management - Introduced AWS SDK dependencies for S3 and CSV handling. - Implemented logic to check and update the default bot configuration in S3 after component installation. - Added a new configuration CSV template for bot settings. - Refactored package manager to register cache component with updated download URL and binary name. - Updated README and Cargo files to reflect new dependencies and configuration options.
2025-10-26 14:15:43 -03:00
llm-server,false
refactor(config): replace raw SQL with Diesel query for bot config Updated `ConfigManager::get_config` to use Diesel query builder instead of raw SQL for improved safety and maintainability. Adjusted parameter naming and integrated schema references. Also refactored `ensure_llama_servers_running` to fetch configuration from the database using `AppState` and `ConfigManager`. Removed unused imports in bootstrap module.
2025-11-01 11:48:46 -03:00
llm-server-path,botserver-stack/bin/llm/build/bin
feat: refactor database connection handling and add LLM component - Replace direct database connection establishment with shared `establish_pg_connection` utility - Add "llm" to required components list in bootstrap manager - Lower default RUST_LOG level from debug to info in VSCode config - Clean up imports and connection error messages - Remove hardcoded database URL strings in favor of centralized connection handling The changes improve code maintainability by centralizing database connection logic and adding support for the new LLM component in the bootstrap process.
2025-11-01 07:20:04 -03:00
llm-server-host,0.0.0.0
refactor(config): replace raw SQL with Diesel query for bot config Updated `ConfigManager::get_config` to use Diesel query builder instead of raw SQL for improved safety and maintainability. Adjusted parameter naming and integrated schema references. Also refactored `ensure_llama_servers_running` to fetch configuration from the database using `AppState` and `ConfigManager`. Removed unused imports in bootstrap module.
2025-11-01 11:48:46 -03:00
llm-server-port,8081
feat: optimize suggestions and LLM server configuration - Added duplicate filtering for suggestions in both backend (Rust) and frontend (JavaScript) - Changed announcement summary schedule from every 15 minutes to hourly at :37 - Simplified LLM prompt for document summarization - Updated LLM server configuration with reduced GPU layers and increased context size - Removed memory mapping and lock settings for LLM server - Improved HTML formatting and added missing newline at EOF
2025-11-03 13:38:55 -03:00
llm-server-gpu-layers,0
llm-server-n-moe,0
feat: improve prompt formatting and system metrics - Update prompt formatting in BotOrchestrator to use clearer labels (SYSTEM/CONTEXT) with emphasis markers - Remove unused token_ratio field from SystemMetrics struct - Increase default context size (2048->4096) and prediction length (512->1024) in config - Clean up metrics calculation by removing redundant token ratio computation The changes improve readability of system prompts and simplify metrics collection while increasing default model capacity.
2025-11-05 12:46:08 -03:00
llm-server-ctx-size,4096
llm-server-n-predict,1024
feat(automation): improve cron matching and job locking - Refactor cron matching to use individual variables for each time component with additional debug logging - Replace SETEX with atomic SET NX EX for job locking in Redis - Add better error handling and logging for job execution tracking - Skip execution if Redis is unavailable or job is already held - Add verbose flag to LLM server startup command for better logging
2025-11-03 13:52:28 -03:00
llm-server-parallel,6
feat: refactor database connection handling and add LLM component - Replace direct database connection establishment with shared `establish_pg_connection` utility - Add "llm" to required components list in bootstrap manager - Lower default RUST_LOG level from debug to info in VSCode config - Clean up imports and connection error messages - Remove hardcoded database URL strings in favor of centralized connection handling The changes improve code maintainability by centralizing database connection logic and adding support for the new LLM component in the bootstrap process.
2025-11-01 07:20:04 -03:00
llm-server-cont-batching,true
feat: optimize suggestions and LLM server configuration - Added duplicate filtering for suggestions in both backend (Rust) and frontend (JavaScript) - Changed announcement summary schedule from every 15 minutes to hourly at :37 - Simplified LLM prompt for document summarization - Updated LLM server configuration with reduced GPU layers and increased context size - Removed memory mapping and lock settings for LLM server - Improved HTML formatting and added missing newline at EOF
2025-11-03 13:38:55 -03:00
llm-server-mlock,false
llm-server-no-mmap,false
feat(llm): pass model configuration to LLM generation and streaming Include model parameter in LLM provider calls across automation, bot, and keyword modules to ensure correct model selection based on configuration. This improves flexibility and consistency in LLM usage.
2025-11-12 08:19:21 -03:00
,
,
Add AWS SDK integration and update bot configuration management - Introduced AWS SDK dependencies for S3 and CSV handling. - Implemented logic to check and update the default bot configuration in S3 after component installation. - Added a new configuration CSV template for bot settings. - Refactored package manager to register cache component with updated download URL and binary name. - Updated README and Cargo files to reflect new dependencies and configuration options.
2025-10-26 14:15:43 -03:00
email-from,from@domain.com
email-server,mail.domain.com
email-port,587
email-user,user@domain.com
email-pass,
feat(llm): pass model configuration to LLM generation and streaming Include model parameter in LLM provider calls across automation, bot, and keyword modules to ensure correct model selection based on configuration. This improves flexibility and consistency in LLM usage.
2025-11-12 08:19:21 -03:00
,
Add AWS SDK integration and update bot configuration management - Introduced AWS SDK dependencies for S3 and CSV handling. - Implemented logic to check and update the default bot configuration in S3 after component installation. - Added a new configuration CSV template for bot settings. - Refactored package manager to register cache component with updated download URL and binary name. - Updated README and Cargo files to reflect new dependencies and configuration options.
2025-10-26 14:15:43 -03:00
custom-server,localhost
custom-port,5432
custom-database,mycustomdb
custom-username,
custom-password,
Fix typos in bot file extensions and keyword names Changed incorrect references to .vbs files to .bas and corrected USE_WEBSITE keyword naming. Also added missing fields to API response structure and clarified that start.bas is optional for bots.
2025-11-26 22:54:22 -03:00
,
website-expires,1d
website-max-depth,3
website-max-pages,100
Add multimodal module for botmodels integration Introduces IMAGE, VIDEO, AUDIO, and SEE keywords for BASIC scripts that connect to the botmodels service for AI-powered media generation and vision/captioning capabilities. - Add BotModelsClient for HTTP communication with botmodels service - Implement BASIC keywords: IMAGE, VIDEO, AUDIO (generation), SEE (captioning) - Support configuration via config.csv for models
2025-11-29 20:40:08 -03:00
image-generator-model,../../../../data/diffusion/sd_turbo_f16.gguf
image-generator-steps,4
image-generator-width,512
image-generator-height,512
image-generator-gpu-layers,20
image-generator-batch-size,1
video-generator-model,../../../../data/diffusion/zeroscope_v2_576w
video-generator-frames,24
video-generator-fps,8
video-generator-width,320
video-generator-height,576
video-generator-gpu-layers,15
video-generator-batch-size,1
botmodels-enabled,true
botmodels-host,0.0.0.0
botmodels-port,8085
default-generator,all
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