- Add begin_response and finish_after_current_speech methods to CallEndCoordinator for better management of speech events.
- Update PromptBrain to utilize new methods, ensuring proper handling of generated closing speech and tool-only calls.
- Enhance tests to verify the correct behavior of speech tracking and response handling in various scenarios, including waiting for audio to finish before ending calls.
- Introduce a new test suite for CallEndCoordinator to validate the interaction with speech frames.
- Introduce greeting context handling in BaseBrain and WorkflowBrain to manage assistant greetings effectively.
- Implement prepare_greeting_context method to add greeting messages to the local context while preserving playback order.
- Update pipeline event handling to ensure greeting timestamps are maintained until the client is ready.
- Enhance tests to verify the correct behavior of greeting context management in various scenarios.
- Remove unused imports and classes from pipeline.py to streamline the codebase.
- Consolidate dynamic variable handling and workflow management in AssistantPage, enhancing clarity and maintainability.
- Update WorkflowEditor to utilize a more modular approach, improving the overall architecture and reducing complexity.
- Enhance the import structure across components for better organization and readability.
- Implement on_client_ready in BaseBrain to handle client-visible state after the app message channel is ready.
- Extend WorkflowBrain with on_client_ready to replay state that may have been emitted before WebRTC data was ready.
- Update pipeline to call on_client_ready when a client connects.
- Enhance tests to verify the correct behavior of on_client_ready in WorkflowBrain.
- Update ConversationRecorder to include source and nodeId metadata in transcripts for better context tracking.
- Introduce optional variable handling in DynamicVariableStore, allowing for unset variables to be rendered as empty without raising errors.
- Refactor WorkflowBrain to apply turn configurations and manage interaction policies dynamically, improving agent responsiveness.
- Implement tests to ensure proper handling of updated session variables and workflow metadata in various scenarios.
- Introduce WorkflowLLMRouter for pre-response LLM routing, allowing agents to determine the appropriate function to call based on user input.
- Implement UserTurnRoutingProcessor to manage user turns before reaching the LLM, ensuring proper routing and handling of user messages.
- Refactor WorkflowBrain to integrate new routing logic and enhance agent stage configuration, including entry modes and resource management.
- Update service factory to support dynamic LLM resource configuration based on workflow settings.
- Add tests for new routing functionality and ensure proper handling of user messages in various scenarios.
- Introduce RuntimeModelResource and RuntimeKnowledgeBase classes to manage workflow resources.
- Update AssistantConfig to include workflow_model_resources and workflow_knowledge_bases for better integration.
- Refactor validation and processing logic in routes and services to accommodate workflow types.
- Implement dynamic variable support for workflow assistants and enhance graph normalization.
- Add ToolExecutor for reusable tool execution across different assistant types.
- Update various services to ensure compatibility with new workflow features and improve error handling.
- Add new HTTP endpoints for handling WebRTC offers and ICE candidates, enhancing the signaling process for voice interactions.
- Introduce dynamic variable decoding from request headers to support flexible offer payloads.
- Refactor existing WebSocket handling to accommodate new offer processing logic.
- Update frontend dependencies to include Pipecat client libraries for improved WebRTC transport management.
- Streamline voice preview functionality by integrating SmallWebRTCTransport for better media handling.
- Introduce dynamic variable definitions in AssistantConfig and Assistant models, allowing for flexible prompt customization.
- Implement validation for dynamic variable names and types in the schema.
- Update backend services and routes to handle dynamic variables in assistant configurations and runtime processing.
- Enhance frontend components to support dynamic variable definitions, including a new editor for managing variables.
- Add tests to ensure proper functionality and validation of dynamic variables in various scenarios.
- Introduce new fields for knowledge retrieval configuration in AssistantConfig and Assistant models, including mode, top_n, and score_threshold.
- Implement KnowledgeRetrievalConfig schema with validation for top_n.
- Update backend services and routes to handle knowledge retrieval settings.
- Enhance frontend components to support knowledge retrieval configuration, including a new dialog for advanced settings.
- Add tests for knowledge retrieval configuration validation and description generation.
- Add new models for `KnowledgeDocument` and `KnowledgeChunk` to manage document ingestion and chunking.
- Implement S3-compatible storage integration for knowledge documents, allowing for file uploads and retrieval.
- Introduce API endpoints for managing knowledge bases and documents, including creation, deletion, and searching.
- Update frontend components to support knowledge base configuration and document management, improving user interaction.
- Enhance backend services for knowledge processing and retrieval, ensuring robust handling of document statuses and errors.
- Introduce a new `turnConfig` field in `AssistantConfig` and `Assistant` models to manage user interaction settings.
- Implement `TurnConfig`, `BargeInConfig`, `VadConfig`, and `TurnDetectionConfig` schemas to define turn management strategies.
- Update the backend to handle turn configuration in the database and during assistant operations.
- Enhance frontend components with a `TurnConfigEditor` for configuring turn settings, including VAD and barge-in strategies.
- Modify existing pages to integrate turn configuration, improving user experience and interaction capabilities.
- Introduce new fields `dify_api_url` and `dify_api_key` in `AssistantConfig` for Dify API integration.
- Update `requirements.txt` to include `dify-client-python` for Dify SDK support.
- Modify `config_resolver` to handle Dify connection information.
- Add a new `globalNode` type in workflow specifications to provide unified settings across workflows.
- Enhance node specifications with additional constraints and default values for better configuration management.
- Update frontend components to support the new `globalNode` type and its properties, improving workflow editor functionality.
- Implement a new API endpoint for duplicating tools in the backend.
- Add a duplicate_tool function in ComponentsToolsPage to handle tool duplication requests.
- Update the UI to include a dropdown menu option for duplicating tools, improving user experience.
- Refactor the remove function to accept a resource object instead of an ID for better clarity and maintainability.
- Implement a new API endpoint for deleting conversations in the backend.
- Enhance the HistoryPage component to include a dropdown menu for conversation actions, allowing users to delete conversations.
- Update the pagination logic to handle conversation removal and improve user experience during deletion.
- Adjust the loading state and error handling for the delete operation, ensuring smooth interaction.
- Introduce new database models for conversation sessions, messages, and artifacts to support conversation history tracking.
- Implement API routes for listing conversations and retrieving detailed conversation data, enhancing user interaction with historical records.
- Add a conversation recorder service to persist conversation messages in real-time without disrupting ongoing calls.
- Update the frontend to display conversation history, including filtering and sorting options, improving user experience.
- Enhance the pipeline to integrate conversation history recording seamlessly during interactions.
- Introduce a new `RuntimeTool` model to encapsulate tool data for runtime sessions, including attributes like `id`, `name`, `function_name`, `type`, and `description`.
- Update the `AssistantConfig` model to include a list of reusable tools, allowing for better management of tools within assistant configurations.
- Modify the `config_resolver` service to fetch and resolve tools associated with assistants, ensuring they are available during runtime.
- Refactor tool-related CRUD operations in the `tools` route to support the new runtime execution model, enhancing the overall tool management system.
- Update documentation and comments to reflect changes in tool execution and configuration handling, improving clarity for future development.
- Introduce a new `sync_default_tools` function in `session.py` to ensure essential reusable tools are created without overwriting existing edits.
- Update the `lifespan` context manager in `app.py` to call `sync_default_tools`, enhancing the initialization process for the application.
- This change improves the management of default tools within the system, ensuring they are available for use while preserving user modifications.
- Introduce a new `Tool` model and `AssistantToolBinding` for managing reusable tools within the application.
- Implement CRUD operations for tools in the new `tools` route, allowing for the creation, retrieval, updating, and deletion of tools.
- Update the `Assistant` model to include a list of tool IDs, enabling assistants to utilize these tools.
- Enhance the backend routes to synchronize tool bindings with assistants, ensuring proper management of tool associations.
- Add frontend components for tool management, including a tool picker in the assistant configuration, improving user experience in tool selection.
- Create a mobile call page to facilitate video calls, integrating camera and microphone selection for enhanced communication capabilities.
- Update API definitions to include tool-related types and operations, ensuring consistency across the application.
- Add a migration script to create the necessary database tables for tools and bindings, supporting the new functionality.
- Introduce a new `auth` module with login, logout, and user verification endpoints for a single admin user.
- Update backend routes to require admin authentication for sensitive operations, enhancing security.
- Modify frontend components to include an authentication provider and gate, ensuring only authorized users can access the application.
- Implement a login page for admin access, improving user experience and security management.
- Update API request handling to redirect unauthorized users to the login page, ensuring proper access control.
- Replace the `config.py` module with a new `settings.py` to streamline environment variable management, focusing on database, CORS, and TURN settings.
- Update references throughout the backend codebase to use the new `settings` module instead of the deprecated `config`.
- Modify the `.env.example` file to reflect the new configuration approach, indicating that model provider credentials should be maintained separately.
- Enhance the `AssistantConfig` model to clarify the source of runtime connection information, ensuring it is injected from model resources rather than relying on defaults from the environment.
- Introduce new user scripts for audio and video management in the Tampermonkey environment, enhancing WebRTC capabilities.
- Integrate Alembic for managing database schema migrations, replacing the previous SQL schema management.
- Update the FastAPI application to synchronize interface definitions at startup.
- Modify the Docker Compose command to run Alembic migrations before starting the API.
- Enhance the Makefile with new commands for database migration and revision management.
- Remove outdated SQL schema and seed files, transitioning to a more dynamic migration approach.
- Add initial migration scripts and configuration for Alembic, ensuring a structured database evolution.
- Extend the AssistantConfig model to include agent_interface_type, agent_values, and agent_secrets for enhanced agent management.
- Update ModelType to include "Agent" for broader capability support.
- Seed new agent models and configurations in the database for Dify, FastGPT, and OpenCode applications.
- Modify the Assistant routes to validate and handle agent capabilities.
- Implement agent resource resolution in the config resolver for runtime configuration.
- Enhance the interface catalog with agent-specific fields and definitions.
- Update frontend components to manage agent configurations, including form handling and state management for agent-related inputs.
- Update vision model authorization logic to rely solely on server-side configuration, removing client offer dependency for vision capabilities.
- Enhance clarity in comments regarding the role of server configurations in enabling vision understanding.
- Update `requirements.txt` to include Pillow for image handling.
- Refactor vision model validation logic in `voice_webrtc.py` to improve error handling for unsupported image input.
- Introduce new functions in `pipeline.py` for image data processing and analysis using vision models.
- Implement `VisionCaptureProcessor` to manage video frame requests for auxiliary vision model analysis.
- Enhance the pipeline to support image input requests and integrate vision model responses into the processing flow.
- Add new database management targets: db-up, db-schema, db-seed-interface-definitions, and db-init to streamline database setup and seeding processes.
- Update db-seed and db-reset dependencies to include new initialization steps for better data integrity.
- Introduce new SQL files for schema definition and interface definitions seeding, ensuring a consistent database structure.
- Refactor existing seed scripts to align with new dependencies and improve clarity in database operations.
- Introduce new fields in AssistantConfig, schemas, and database models to support vision capabilities, including `vision_enabled` and `vision_model_resource_id`.
- Enhance validation logic in routes to ensure proper handling of vision models and their requirements.
- Update the AssistantPage and related frontend components to include options for enabling vision understanding and selecting appropriate vision models.
- Modify database seed scripts to include vision-related data for assistants, ensuring consistent setup.
- Refactor related functions to integrate vision model handling in the audio-visual processing pipeline.
- Introduce a new `support_image_input` field in model resources, allowing models to indicate support for image input.
- Update the backend models, schemas, and database seed scripts to accommodate the new field.
- Enhance the AssistantConfig and related routes to handle image input capabilities, ensuring proper validation and error handling.
- Modify the frontend components to include toggles for enabling visual understanding and filtering models based on image input support.
- Implement necessary adjustments in the voice preview and pipeline to integrate video stream handling alongside audio functionalities.
- Introduce a new `.env.example` file for environment variable setup, including `PUBLIC_IP`, `TURN_SECRET`, and `TURN_URLS` for WebRTC TURN server configuration.
- Update `docker-compose.yaml` to support TURN server deployment with necessary environment variables and commands.
- Enhance backend configuration and routes to include WebRTC ICE server settings, allowing for STUN/TURN server integration.
- Implement a new service for managing WebRTC ICE server configurations, providing credentials for TURN when configured.
- Modify frontend API to fetch ICE server configurations dynamically, improving support for cross-network voice preview.
- Add new fields in AssistantConfig for FastGPT connection details, including `fastgpt_api_url`, `fastgpt_api_key`, and `fastgpt_app_id`.
- Update the pipeline to utilize the new FastGPT configuration, ensuring proper integration with external services.
- Introduce type handling for different assistant types, including support for realtime modes and external brain management.
- Refactor frontend components to include hints for FastGPT configuration inputs, improving user guidance during setup.
- Introduce a new parameter `audio_out_end_silence_secs` in the `_base_params` function to control the duration of silence added after the end frame, allowing for smoother call termination.
- Set the default value to 0 to ensure immediate hang-up after the end speech, enhancing user experience during call endings.
- Introduce mechanisms in the pipeline to ensure that the end call process waits for the completion of the end speech before hanging up, improving user experience during call termination.
- Update the useVoicePreview hook to handle server-initiated call endings gracefully, distinguishing between normal and error disconnections.
- Adjust TTS stop frame timeout settings to optimize the timing of call terminations, ensuring timely responses without unnecessary delays.
- Refactor related components to support the new end call logic, enhancing overall workflow management and user interaction.
- Introduce edge transition speech functionality in the WorkflowEngine to provide optional speech during node transitions.
- Update pipeline execution to utilize the new transition speech feature, enhancing user experience by masking delays during transitions.
- Modify frontend components to support transition speech in edge specifications, allowing users to define and edit transition speech for edges.
- Refactor edge handling logic in the WorkflowEditor to accommodate the new transition speech field, improving workflow management capabilities.
- Change the 'addable' property of a specific node type to true, allowing for dynamic addition of nodes.
- Modify the GenericNode component to include a new icon and adjust styles for better visual representation.
- Update node handling logic to prevent deletion of 'startCall' nodes and improve node change handling in the workflow editor.
- Refactor layout and styling in the WorkflowEditor for a more polished user interface.
- Introduce a new WorkflowEngine class to manage workflow graphs, enabling dynamic node-based interactions.
- Update AssistantConfig to include a graph field for workflow definitions, allowing for flexible configuration.
- Modify pipeline execution to support workflow-driven dialogue, integrating node transitions and system prompts based on active nodes.
- Enhance frontend components to visualize active nodes and provide debugging capabilities, including highlighting the current node during interactions.
- Refactor existing components to accommodate new workflow functionalities and improve overall user experience.
- Introduce a new workflow editor component for visualizing and managing workflows, allowing users to add nodes and define connections.
- Implement backend support for node types, including validation and constraints for workflow graphs.
- Add new API endpoints for retrieving node types and their specifications.
- Enhance the AssistantPage to integrate the workflow editor, enabling users to create and edit workflows directly.
- Update frontend components to support new workflow functionalities, including condition edges and generic nodes.
- Refactor existing code to accommodate the new workflow features and improve overall structure.
- Add new fields to AssistantConfig for realtime interface configuration, including types, values, and secrets.
- Introduce StepFunRealtimeService to handle speech-to-speech processing via WebSocket, integrating STT, LLM, and TTS functionalities.
- Refactor pipeline execution to support a new realtime mode, allowing direct text input processing and immediate responses.
- Update model resource testing to include validation for StepFun Realtime connections.
- Enhance service factory to create realtime services based on configuration settings.
- Modify README documentation to reflect new realtime capabilities and usage instructions.
- Introduce event handlers in PassthroughLLMAssistantAggregator for managing LLM text streaming, including start, delta, and end events.
- Implement a new method to finalize text streams, ensuring proper handling of interruptions.
- Update useVoicePreview to support new message types for LLM text streaming, allowing real-time updates to chat messages.
- Enhance message sorting logic to maintain order based on timestamps and sequence numbers, improving user experience during voice interactions.
- Refactor TextInputProcessor to handle immediate and silent text inputs, improving user experience during voice interactions.
- Introduce PassthroughLLMAssistantAggregator to manage LLM responses while preserving context for downstream TTS processing.
- Update event handling for text input and client readiness, ensuring timely updates to the conversation context.
- Modify run_pipeline to integrate new aggregators and streamline message handling, enhancing overall pipeline efficiency.
- Improve message ordering in useVoicePreview to ensure accurate display of chat messages based on timestamps.
- Introduce a new model structure for managing interface definitions and model resources, enhancing the backend's capability to handle various service integrations.
- Update the Makefile to reflect changes in database seeding and resource management commands.
- Remove the deprecated credentials management routes and replace them with a unified model registry API.
- Modify existing routes and schemas to align with the new model structure, ensuring seamless integration with the frontend.
- Enhance database seeding scripts to populate new model resources and their configurations.
- Update README documentation to reflect the new architecture and usage instructions for model resources and interface definitions.
- Introduce new Xfyun ASR and TTS services, enabling integration with iFlytek's voice recognition and synthesis capabilities.
- Update AssistantConfig model to include interface types for STT and TTS.
- Enhance credential testing to validate Xfyun credentials.
- Modify service factory to create Xfyun services based on configuration.
- Update README with new configuration details for Xfyun integration.
- Add new frontend components for visualizing audio streams and managing user interactions.
- Add a new Docker configuration for the UI in launch.json to facilitate development.
- Refactor pipeline.py to integrate a TranscriptProcessor for managing user and assistant transcripts, including event handlers for real-time updates and message handling.
- Update useVoicePreview.ts to establish a data channel for sending and receiving text messages, improving interaction flow.
- Modify AssistantPage.tsx to support displaying chat messages and sending user input, enhancing the user experience during voice interactions.
- Revise DebugTranscriptPanel to dynamically render chat messages with timestamps, improving the visual representation of conversation history.
- Introduce `setup-certs.sh` script for generating trusted local TLS certificates using mkcert.
- Add Nginx configuration files for local and Docker environments to handle HTTPS requests and proxy to backend services.
- Update `docker-compose.yaml` to include Nginx service for unified TLS entry and adjust frontend service ports for local development.
- Create `AGENTS.md` and `README.md` files to document the local HTTPS setup process and usage instructions.
- Modify backend startup commands in `README.md` for consistency with new requirements.
- Add `.gitignore` to exclude generated certificates from version control.
- Update README to reflect the integration of the DebugVoicePanel with WebSocket support for voice interactions.
- Refactor voice_webrtc.py to improve error handling during WebRTC signaling and include assistant_id in the offer payload.
- Add useVoicePreview hook to manage microphone access and WebRTC connections for real-time voice previews.
- Modify AssistantPage to incorporate new visualizer options and pass assistantId to DebugVoicePanel, enhancing user experience during audio interactions.
- Update API model to include new fields for voice, speed, and language, supporting TTS and ASR configurations.
- Introduce new fields for voice, speed, and language in the AssistantConfig and ProviderCredential models to support TTS and ASR configurations.
- Update the database schema and seeding script to accommodate the new fields, ensuring backward compatibility.
- Implement credential testing endpoints and logic to validate OpenAI-compatible credentials, enhancing user experience and reliability.
- Modify frontend components to include new fields in the credential forms and improve connection testing feedback.
- Refactor related services and API interactions to support the new credential testing feature.
- Change 'DeepSeek-V3' to 'DeepSeek-Chat' and update its API key.
- Rename 'OpenAI TTS' to 'SiliconFlow-CosyVoice2-0.5B' and update its details.
- Add new models: 'SiliconFlow-TeleSpeechASR' and 'SiliconFlow-Qwen3-Embedding-4B' with corresponding API keys and configurations.
- Adjust existing entries to ensure consistency in the database seeding process.
- Update Makefile to include new database seed commands for assistants and credentials.
- Refactor assistant model to use explicit fields instead of a config dictionary, improving data integrity and clarity.
- Implement new seeding SQL script for assistants, ensuring dependencies on credentials are respected.
- Modify backend routes and frontend components to accommodate the new assistant structure, including direct field access for prompt, API URL, and keys.
- Enhance the AssistantPage component to handle the new data structure and streamline the save process for different assistant types.
