- 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.
- Enhanced the extractSandboxVersion function to support multiple input types, including *sandboxTypes.SandboxConfig and map[string]any, for better flexibility in version retrieval.
- Simplified the version extraction process, ensuring consistent handling of sandbox configurations.
- Added support for sandbox configuration in the LoadPath function, allowing for better management of sandbox settings.
- Updated tests to validate the retrieval of assistant tags with type filters, ensuring accurate responses for both assistant and robot types.
- Enhanced the assistant model to differentiate between sandbox versions, improving the handling of sandbox data in the database.
- Improved API responses to include computer filter details for V2 sandboxes, enhancing the information returned to clients.
- Updated the AssistantInfo struct to include new fields: Connector, ConnectorOptions, Modes, DefaultMode, Sandbox, and ComputerFilter for improved assistant configuration.
- Enhanced the loading process to extract Sandbox flag and ComputerFilter from V2 sandbox configuration.
- Refactored GetInfo method to return comprehensive assistant details for better UI integration.
- Introduced new endpoint for workspace options to streamline InputArea selector functionality.
Made-with: Cursor
- Implemented V2 sandbox initialization in the assistant loading process, allowing for standalone sandbox.yao configuration.
- Added support for V2 sandbox execution paths in the Assistant's Stream method, differentiating between V1 and V2 sandboxes.
- Introduced comprehensive tests for V2 sandbox configurations, ensuring correct loading and execution behavior.
- Updated the context and types to accommodate V2 sandbox features, including system information and workspace management.
Made-with: Cursor
- Introduce a new field for dependencies in the Assistant model to manage external MCP client dependencies with version constraints.
- Implement deep copy functionality for dependencies in the Clone method to ensure integrity during assistant cloning.
- Enhance the Update method to handle dependencies input from data maps, supporting both string and interface types.
- Update the Map method to include dependencies in the serialized output.
- Add comprehensive tests to validate loading, cloning, and mapping of dependencies, ensuring correct behavior across various scenarios.
- Introduce `Capabilities` and `Sandbox` fields in the Assistant model, allowing for detailed descriptions of assistant capabilities and sandbox configurations.
- Update loading and conversion functions to handle the new fields, ensuring they are correctly parsed and stored.
- Modify filtering and response handling to include the new fields, providing better integration with the API.
- Add comprehensive tests to validate the functionality of the new fields, ensuring they are correctly processed in various scenarios.
- 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.
- Eliminate the `openai` field from the `Assistant` struct and its initialization in the `initialize` method, streamlining the Assistant's internal structure.
- This change simplifies the codebase by removing unnecessary dependencies on the OpenAI API, enhancing maintainability.
- Added steps to pull necessary Docker images for sandbox testing in both CI workflows.
- Updated the AI test execution to utilize sandbox configurations, ensuring proper environment setup.
- Introduced sandbox initialization in the Assistant's Stream method, allowing for execution of coding agents like Claude and Cursor.
- Enhanced context management to support sandbox execution, improving flexibility in handling agent operations.
- Updated the BuildContent method to utilize content parsing with improved error handling and context injection, enhancing the processing of user input.
- Refactored the loadMap function to support multiple search configuration types, improving flexibility in handling search settings.
- Enhanced the shouldAutoSearch method to include a check for search disabling via context metadata, allowing for more granular control over search behavior.
- Updated the getMergedSearchUses method to prioritize options.Uses, ensuring that search configurations can be dynamically adjusted based on provided options.
- Removed deprecated audio and excel handling code, streamlining the content processing package and improving maintainability.
- Renamed several assistant packages for clarity, including "Entity Extraction" to "Entity Extractor" and "Keyword Extraction" to "Keyword Extractor."
- Revised descriptions for various assistants to enhance understanding of their functionalities, such as changing "Extract keywords from text content" to "Extract search keywords."
- Added a "uses" field with "search" set to "disabled" in the configuration of each assistant, standardizing their setup.
- Updated the "Prompt Optimizer" description to "Optimize prompts for better results" and modified the "QueryDSL Generator" to "Query Builder" for improved clarity.
- Ensured consistent naming conventions and descriptions across all assistant packages to enhance user experience and documentation clarity.
- Updated the `shouldAutoSearch` method to include additional parameters for improved intent detection, allowing for better decision-making on whether to execute auto search.
- Introduced a new `checkSearchIntent` method to utilize the `__yao.needsearch` agent for determining the necessity of a search based on user input.
- Implemented a `ClearExcept` method in the cache to selectively clear non-system agents while preserving essential system agents during cache management.
- Updated the `LoadBuiltIn` function to maintain system agents in the cache, ensuring they remain available for use.
- Enhanced test coverage for loading system agents and validating search intent detection, ensuring robustness in the assistant's search capabilities.
- Revised localization files to include new messages for search intent feedback, improving user experience during search operations.
- 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.
- Updated cache tests to use the testify assertion library for improved readability and maintainability.
- Added new tests for cache operations including basic functionality, LRU eviction, removal, clearing, and concurrent access.
- Enhanced the cache implementation to unregister scripts upon removal and clearing, ensuring proper resource management.
- Introduced an `All` method to retrieve all assistants in the cache, improving accessibility of cached items.
- Streamlined the script registration and unregistration process within the Assistant struct, enhancing script management.
- Updated the script loading process to load both hook scripts and other scripts from the src directory, improving organization and clarity.
- Introduced a new `Scripts` field in the Assistant struct to store additional scripts, enhancing the flexibility of script management.
- Removed deprecated script loading functions and streamlined the loading logic to ensure better maintainability and performance.
- Adjusted the handling of timestamps for script updates, ensuring accurate tracking of script modifications.
- Replaced all instances of `Script` with `HookScript` in the Assistant and related files to improve clarity and consistency in naming.
- Updated method calls in the Stream, Create, and Next hooks to utilize the new `HookScript` field.
- Adjusted tests and benchmarks to reflect the changes in script handling, ensuring all functionalities remain intact and operational.
- Enhanced the load and initialization processes to accommodate the new HookScript structure, streamlining the assistant's script management.
- Updated the Stream, BuildContent, and LLM execution methods to accept an Options parameter, allowing for more flexible context handling.
- Removed debug print statements to clean up the code and improve readability.
- Enhanced locale handling in the loadMap function to automatically inject assistant name and description into all locales, ensuring better localization support.
- Introduced output skipping functionality in context options to manage internal A2A calls more effectively.
- Improved output writer resolution logic to prioritize context settings, enhancing output management during agent calls.
- Added support for converting extended types (file, data) to standard LLM types (text, image_url, input_audio) in the Stream method.
- Introduced a new agentCallerWrapper to facilitate agent calls from the content package.
- Implemented ThreadID management for nested agent calls to improve concurrent stream identification.
- Enhanced message handling to include metadata for message_start and message_end events, allowing for better tracking of message states.
- Removed deprecated vision capability checks from the Assistant initialization process, streamlining the codebase.
- Added new content types (file, data) to the context types for improved message content handling.
- Enhanced the `loadMap` function to include handling for `modes` and `default_mode` fields, allowing for flexible operational modes and a specified primary mode.
- Introduced a new `DB` field in the Assistant model to support database configuration, improving data management capabilities.
- Implemented the `ToModes` conversion function to facilitate various input types for modes, ensuring robust handling and validation.
- Added comprehensive tests for the `ToModes` function to validate its functionality across different input scenarios, enhancing overall reliability.
- Updated relevant methods to ensure consistent integration of the new fields and functionalities within the Assistant model.
- Introduced GetStorage function for testing purposes, allowing retrieval of the current storage instance.
- Enhanced loadSource function to load scripts from the source field if present, improving assistant initialization.
- Updated comments for clarity on TypeScript handling in loadSource, ensuring better understanding of script loading mechanics.
- 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.
- Removed the GetByConnector method from the Assistant model to streamline the retrieval process.
- Introduced new fields for connector options and prompt presets, allowing for more flexible configurations.
- Updated the Map method to include additional fields such as connector options and prompt presets.
- Enhanced the Clone method to support deep copying of new fields.
- Improved the Update method to handle updates for the new source, connector options, and prompt presets fields.
- Refactored tests to ensure comprehensive coverage of the new functionalities and maintain clarity in the assistant structure.
- Enhanced the getConnectorCapabilities method to prioritize model capabilities and connector settings, improving capability retrieval logic.
- Deprecated the tools field in the Assistant model, transitioning to MCP for tool management, and updated related methods accordingly.
- Introduced new fields for connector options and prompt presets in the Assistant model, allowing for more flexible configurations.
- Updated the GetAssistant method to support field selection, improving data retrieval efficiency and flexibility.
- Refactored tests and documentation to reflect changes in the assistant structure and capabilities, ensuring clarity and maintainability.
- 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.
- 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.
- Updated the Stream method to return a structured response, including create, done, and completion hooks.
- Introduced new methods for managing message history and building LLM requests, enhancing modularity.
- Refactored the Script type to utilize a new hook structure, improving code organization.
- Removed the obsolete hooks file to streamline the codebase.
- Enhanced context types with additional fields for better integration with LLM models.
- 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.
