Changed tool output serialization in AgentService._convert_event_to_chunk()
from str() to json.dumps() to ensure structured data (dict/list) is properly
formatted as standard JSON instead of Python string representation.
Changes:
- Added json import to agent_service.py
- Modified on_tool_end event handling to use json.dumps(output, ensure_ascii=False, indent=2)
- Updated ai-chat-api-design.md to document tool_output format
Benefits:
- Frontend can parse tool results with standard JSON.parse()
- Chinese and non-ASCII characters are preserved (not escaped)
- Formatted output (indent=2) improves readability
Co-Authored-By: YueGuobin <yueguobin@outlook.com>
Updated the author name and copyright statements across the
gns3_copilot module. The name has been standardized from
"Guobin Yue" to "Yue Guobin (岳国宾)" to reflect the correct
author attribution including Chinese characters.
- Improve POST /chat endpoint documentation with request/response examples
- Add session ID management flow explanation
- Enhance GET /sessions endpoint with query parameters and response example
- Update GET /sessions/{session_id}/history with detailed response structure
- Format parameters as tables for better readability
- Clarify session ID usage in streaming conversations
Updated references to the title generation node in both documentation and code:
- Changed node name from `generate_title` to `title_generator_node` in API design documentation
- Updated filtering logic in agent service to exclude `title_generator_node` from LLM call statistics, token counting, and frontend streaming events
- Maintains same functionality while using more descriptive node name for clarity
Filter out internal LangGraph 'generate_title' node from LLM call counting and token usage tracking to avoid inflating statistics with internal operations. This ensures metrics only reflect user-facing AI interactions.
- Update API documentation to reflect new streaming tool call mechanism
- Add `message_id` optional field to content and tool_call events
- Change tool_call structure from array to single object with incremental updates
- Add `tool_call_id` to tool_start events for better event correlation
- Implement ToolCallStreamAccumulator class to handle parameter accumulation
- Provide frontend example code for handling streaming tool calls
- Maintain backward compatibility with existing session_id tracking
- Update AI chat API documentation with revised SSE event schema
- Add support for multiple tool calls in `tool_call` events
- Include `session_id` in all event types for better session tracking
- Implement `on_chat_model_end` handler to process LLM tool call decisions
- Update example JSON payloads to reflect new schema structure
- Added `temperature` parameter to Chat API documentation with implementation notes
- Improved code formatting in context_manager.py with consistent string quotes and line breaks
- Added section on future runtime LLM parameter override capabilities
- Updated API schemas to include temperature parameter (currently unused but reserved for future implementation)
- Extract context variable management functions from connector_factory.py to new context_helpers.py module
- Update imports in gns3_copilot.py, agent_service.py, and __init__.py to use new module
- Remove inline imports and ensure consistent access to context helpers
- Improves code organization and maintainability by separating concerns
Add copyright notice and author attribution to multiple Python files in the gns3-copilot module. This ensures proper licensing attribution and clarifies authorship for the project files.
Removed repetitive "This module is part of the GNS3-Copilot project" and GitHub URL comments from multiple module docstrings. These comments were redundant since the project information is already established in the main package documentation. This cleanup improves code readability and reduces maintenance overhead by eliminating duplicate attribution statements across the codebase.
- Refactor system message structure to combine system prompt and topology info using template variables
- Update token calculation process with merged system message approach
- Clarify priority order for message retention during context window management
- Add detailed token counting implementation using tiktoken library
- Include boundary case handling for system message exceeding budget
- Add SPDX license headers to tool files for proper licensing documentation
- Add `pinned` column to chat_sessions table with default FALSE
- Implement database migration for existing installations using PRAGMA table_info
- Create composite index for pinned + updated_at sorting
- Add pin/unpin API endpoints (PUT/DELETE /sessions/{id}/pin)
- Update session listing to sort by pinned status then updated_at
- Extend ChatSessionsRepository with pin_session method
- Update API documentation to reflect new pinning functionality
The feature allows users to pin important chat sessions to the top of the list. Sessions are sorted with pinned sessions first (by updated_at), followed by regular sessions (by updated_at). Database migration ensures backward compatibility with existing installations.
- Add message ID generation for initial HumanMessage creation
- Implement message converters for LangChain/OpenAI format interoperability
- Update documentation with detailed message format specifications
- Refactor AgentService to use centralized message conversion utilities
- Ensure tool_calls format compliance with OpenAI API standards
Update the AI chat API design documentation to provide comprehensive details about statistics collection during streaming conversations. The documentation now includes:
1. **Enhanced statistics collection logic**:
- Clarified message_count increments for user messages, AI responses, and tool results
- Added LLM call tracking via on_chat_model_start events
- Detailed token counting methodology using LangGraph's usage_metadata
2. **Improved implementation details**:
- Added specific event handlers for statistics collection
- Explained LangGraph's cumulative token counting behavior
- Provided real-world examples of token accumulation
3. **Updated data models**:
- Enhanced ChatSession model documentation with field descriptions
- Separated fields into categories (basic, statistics, timestamps, reserved)
4. **Refined architecture documentation**:
- Added detailed flow for stream_chat method
- Documented statistics collection mechanism during SSE streaming
- Explained batch update strategy to reduce database writes
The changes ensure developers understand how conversation statistics are collected, processed, and stored without impacting streaming performance.
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
