- Updated the system configuration to include new role-level defaults for Light, Vision, and Audio connectors.
- Refactored the resolveSystemConnector function to prioritize per-agent overrides, improving connector resolution logic.
- Enhanced LLMConnector integration across various components to streamline settings retrieval and capabilities management.
- Improved error handling and logging for connector-related operations, ensuring better diagnostics and user feedback.
- Replaced 'Voice' with 'Audio' in the system configuration and related tests to better reflect functionality.
- Introduced new methods for role management in the llmprovider, allowing for dynamic retrieval of roles based on user and team context.
- Updated the OpenAPI settings to support new role management endpoints and capabilities.
- Enhanced the handling of API keys in provider management, allowing for optional plain-text retrieval.
- Introduced Vision and Voice fields in the SystemConfig and System types to support new capabilities.
- Updated resolveEnvStrings function to handle environment variables for Vision and Voice.
- Enhanced unit tests to validate the new Vision and Voice configurations, ensuring correct environment variable resolution.
- Added GetVisionConnector and GetVoiceConnector functions to retrieve connectors for vision and voice capabilities.
- Introduced a new global phase agent resolver to streamline agent ID retrieval for various robot pipeline phases, enhancing flexibility in agent configuration.
- Updated existing phase agent retrieval logic to prioritize per-robot configurations, falling back to global settings when necessary.
- Enhanced error handling to provide clearer messages when no agent is configured for specific phases.
- Added tests to validate the new resolution logic and ensure proper functionality across different configurations.
- Introduce `resolveEnvStrings` function to handle `$ENV.XXX` references in the agent's DSL settings, ensuring that environment variables are correctly substituted in system and uses fields.
- Enhance `Load` function to call `resolveEnvStrings` during the loading process, improving the configuration handling for agents.
- Add comprehensive unit tests for `resolveEnvStrings` to validate the correct resolution of environment variables across various fields and scenarios, including handling of undefined variables and plain strings.
- Ensure that the implementation maintains existing functionality while enhancing flexibility for environment-based configurations.
- Remove the `LoadModelCapabilities` test and associated model capabilities initialization from the agent, streamlining the loading process.
- Update the LLM provider implementations to utilize a unified `Capabilities` structure, replacing references to `openai.Capabilities` with `llm.Capabilities`.
- Enhance capability retrieval methods to simplify the extraction of connector capabilities, ensuring compatibility across different LLM providers.
- Clean up unused functions and variables related to model capabilities, improving code maintainability.
- 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.
- Updated the search handling to incorporate keyword extraction with weights, improving the relevance of search results.
- Refactored the `shouldAutoSearch` method to return a `SearchIntent` struct, allowing for more nuanced control over search execution based on context.
- Enhanced the `buildSearchRequests` function to utilize extracted keywords, optimizing search queries based on user input.
- Improved the handling of search types and conditions, ensuring that the system can dynamically adjust search behavior based on intent and configuration.
- Updated documentation and prompts to reflect changes in keyword extraction and search intent classification, providing clearer guidelines for usage.
- 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 a new method to set store settings during assistant initialization, allowing for configuration of storage parameters such as MaxSize and TTL.
- Updated context creation methods to streamline the setup process, ensuring that essential fields are populated consistently across various test contexts.
- Revised tests to validate the new initialization behavior and context management, ensuring proper handling of assistant settings and context properties.
- 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.
- Added methods to set and retrieve global prompts, enhancing the assistant's capabilities.
- Updated the assistant's message building process to include global prompts, ensuring context-aware parsing.
- Introduced tests to validate the integration and functionality of global prompts within the assistant.
- Improved context variable handling for prompt parsing, supporting dynamic content generation.
- 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.
- 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.
- Upgraded Go version to 1.25 and updated several dependencies, including `testify` to v1.11.1 and added new indirect dependencies for JSON schema validation.
- Refactored the assistant's context management to utilize a new `context.Uses` structure, improving the handling of vision, audio, search, and fetch configurations.
- Enhanced the assistant's request building process to support new response formats, including JSON schema validation, ensuring better integration with various tools and services.
- 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.
- Updated import paths for store packages to use specific types for better clarity and maintainability.
- Refactored store initialization in the agent to utilize the new store types for Mongo, Redis, and Xun.
- Removed deprecated store-related files to streamline the codebase and improve overall organization.
- Ensured that all references to the store in the agent files are consistent with the new structure.
- Updated assistant data handling to utilize structured models, improving code clarity and maintainability.
- Enhanced the assistant save and load functions to work with the new AssistantModel structure.
- Refactored mention handling to streamline the conversion process from raw data to structured mentions.
- Improved error handling in the assistant save process, ensuring better feedback for invalid data.
- Updated API responses to use the new gin.H format for consistency across the application.
- Deleted session-related functions (UserID, GuestID, UserRoles, UserOrGuestID) from the agent package to streamline the codebase.
- Removed RAG-related code and references, simplifying the agent's architecture.
- Updated API and load functions to reflect these changes, ensuring consistency across the agent module.
- Enhanced test coverage by cleaning up deprecated test cases related to removed functionalities.
Remove deprecated studio package and refactor agent integration
- Deleted the studio package, which is no longer in use, to streamline the codebase.
- Updated references in the agent and chat modules to utilize the new agent package instead of the deprecated neo package.
- Ensured that all related middleware and routing functionalities are now aligned with the agent architecture, enhancing overall system coherence.
