- Added support for a new `RobotPrompt` field in the `Uses` struct to allow for custom robot system prompts.
- Updated the `Load` function to initialize `RobotPrompt` with a default value if not provided.
- Modified the `SystemConfig` struct to include a connector for the new `RobotPrompt` agent.
- Enhanced the asset binding to include new files related to the `robot_prompt` assistant.
- Updated related documentation and tests to reflect the addition of the `RobotPrompt` functionality.
- Added configuration support for system agents in the assistant initialization process, allowing for custom connectors for agents like __yao.keyword and __yao.querydsl.
- Implemented the loading mechanism for system agents from bindata, ensuring that essential agents are available during runtime.
- Updated the LoadBuiltIn function to exclude system agents from being removed, enhancing the management of built-in and system agents.
- Enhanced test coverage by introducing tests for loading system agents, verifying their presence and correctness in the cache.
- Updated documentation to reflect the new system agents configuration and loading processes.
- Introduced a new search configuration structure, allowing for detailed settings for web, knowledge base, database, citation, and weights.
- Updated the `Assistant` model to include a `Search` field, enabling assistant-specific search configurations.
- Enhanced the loading and merging logic for search configurations, ensuring global defaults can be overridden by assistant-specific settings.
- Added comprehensive tests for loading, saving, and updating assistants with search configurations, verifying the integrity of search settings.
- Updated documentation in DESIGN.md to reflect the new search configuration hierarchy and usage, clarifying the interaction between global and assistant-level settings.
- Added functionality to load Knowledge Base (KB) configuration from `agent/kb.yml`, allowing dynamic settings for chat sessions.
- Introduced `initKBConfig` function to read and parse KB settings, integrating them into the Assistant's initialization process.
- Enhanced the Assistant's conversation initialization to prepare KB collections asynchronously, improving performance during chat interactions.
- Updated tests to verify the correct loading and application of KB settings, ensuring robust integration with the Assistant's functionality.
- Refactored metadata handling for KB collections to include additional fields for improved context management during chat sessions.
- Introduced the `disable_global_prompts` field in the Assistant model to control the usage of global prompts.
- Updated the Load and Get methods to initialize and retrieve global prompts from the configuration.
- Refactored tests to validate the new global prompts functionality and ensure proper loading and context handling.
- Improved the overall structure and clarity of the assistant's capabilities and configurations.
- Updated the model capabilities throughout the agent to utilize the new gouOpenAI.Capabilities struct instead of the previous ModelCapabilities.
- Adjusted related methods and types to ensure compatibility with the new capabilities structure, enhancing clarity and maintainability.
- Improved context handling and message processing by directly integrating OpenAI capabilities, streamlining the overall architecture.
- Introduced a new messageMetadataStore type for managing message and block metadata with thread-safe operations.
- Added methods for setting, getting, and updating message and block metadata, enhancing concurrency handling.
- Removed redundant metadata store implementation from types.go, centralizing functionality in message.go for improved organization.
- Removed deprecated API and vision components, streamlining the agent's architecture.
- Updated the Load function to utilize a new agentDSL variable, enhancing the management of assistant capabilities.
- Enhanced context handling by introducing a message metadata store for thread-safe operations, improving message tracking and management.
- Refactored context methods to eliminate deprecated fields, ensuring cleaner and more maintainable code.
- Improved documentation and comments throughout the codebase to clarify changes and enhance developer understanding.
- Updated the Load function and API handlers to replace 'Use' with 'Uses' for assistant configuration, enhancing consistency across the codebase.
- Modified related structures and functions to reflect the new naming convention, ensuring better alignment with the intended functionality.
- Added a new Skip struct to manage request skip configurations, improving request handling flexibility.
- Implemented tests for the GetSkip function to validate skip parameter extraction from both request body and query parameters.
- Renamed and refactored functions and variables to transition from connector settings to model capabilities, enhancing clarity and consistency.
- Updated the loading mechanism to read model capabilities from `models.yml` instead of `connectors.yml`.
- Adjusted the assistant's global settings to utilize model capabilities, ensuring proper integration with the new configuration structure.
- Enhanced the reasoning adapter to support temperature adjustment based on model capabilities, improving flexibility in handling reasoning parameters.
- Added global uses configuration to the assistant, allowing for centralized management of vision, audio, search, and fetch settings.
- Updated the Assistant struct and related methods to support the new Uses configuration, improving flexibility in assistant operations.
- Refactored the Stream method to utilize the new CompletionResponse type, enhancing response handling.
- Introduced new methods for building requests and managing capabilities, streamlining the assistant's interaction with various connectors.
- Deleted obsolete agent API files (agent.go, api.go, api_test.go, types.go) to streamline the codebase.
- Refactored the agent loading logic to initialize the API instance correctly, ensuring proper integration with the new structure.
- Updated context handling to improve clarity and maintainability across the agent's functionality.
- Enhanced error handling and cache management in the agent's initialization process.
