Add initialization check for `_checkpointer_conn` in `AgentService` to ensure the checkpointer database connection exists before attempting to retrieve chat sessions. This prevents potential null reference errors when the checkpointer hasn't been initialized yet during agent operations.
- Remove `selected_project` tuple from MessagesState as it's no longer needed
- Replace tuple-based project selection with direct `project_id` from config
- Remove unused `mode` parameter from llm_call function
- Update topology retrieval to use project_id directly from configurable settings
- Streamline context messages by removing redundant project info formatting
- Move project_id from metadata to configurable section in agent_service config
- Add info and debug logging to llm_call node for tracking LLM invocations and configuration
- Add error handling and logging to tool_node for tool execution failures
- Add startup logging to stream_chat method with session details
- Improve observability of agent workflow and debugging capabilities
The `stream_chat` method was yielding a "done" message after streaming all chunks, but this is unnecessary as the streaming completion is already indicated by the end of the stream. Removing this redundant message simplifies the response handling and aligns with typical streaming patterns.
- Refactor `llm_call` and `generate_title` nodes to retrieve `llm_config` from request-scoped context variable instead of LangGraph config
- Remove `jwt_token` and `llm_config` from LangGraph configurable parameters in `AgentService.stream`
- Add `set_current_llm_config` and `get_current_llm_config` functions to `connector_factory` and export them in `__init__.py`
- Update `tool_node` to no longer extract `jwt_token` from config as it is now handled via context variable
- Improves thread safety and decouples configuration from LangGraph's state management
- Move JWT token from state to configurable context for better security and request isolation
- Add user_id parameter to agent service for enhanced metadata tracking
- Update checkpoint directory name from .gns3-copilot to gns3-copilot
- Implement context-aware JWT token management using ContextVar
- Improve tool node to extract JWT token from config instead of state
Update agent service to properly access content from AIMessageChunk objects during chat model streaming. Instead of using dictionary get method on the chunk, now use getattr to directly access the content attribute, ensuring compatibility with the AIMessageChunk object structure.
- Replace separate user_id and jwt_token parameters with unified llm_config dict
- Simplify model factory to accept llm_config directly instead of fetching from API
- Update llm_call and generate_title nodes to extract llm_config from LangGraph config
- Remove deprecated API fetching logic from model factory
- Maintain backward compatibility for existing tool usage patterns
This change centralizes LLM configuration management, reducing API calls and improving performance by passing configuration directly from the API layer rather than fetching it repeatedly.
Updated import statements across multiple agent files to use absolute paths starting with `gns3server.agent.gns3_copilot` instead of relative `gns3_copilot` imports. This ensures proper module resolution within the gns3-server package structure and prevents import errors when the agent is executed from different contexts.
- Modified `llm_call` and `generate_title` functions to accept `config` parameter, extracting `user_id` and `jwt_token` for per-user LLM configuration and API authentication
- Updated `create_base_model_with_tools` and `create_title_model` calls to pass user authentication details
- Added `jwt_token` to state for tool usage in GNS3 API calls
- Integrated chat router into controller API routes under `/chat` endpoint
- Implemented `_cleanup_copilot_agent` method in `Project` class to remove AgentService resources upon project closure, preventing resource leaks
- Enhanced error handling in agent cleanup to avoid interrupting project close operations
