Simplify the documentation structure in `README.md` by removing the
`todo/` directory reference and detailed design documents for planned
features. Consolidate future roadmap items into a high-level summary
under "Future Enhancements".
Update `node-control-tools.md` to include documentation for new topology
management tools (create node, create link, get template, rename node)
and reflect updated API imports for `Link` support.
- Add GNS3StopNodeTool and GNS3SuspendNodeTool to lab automation assistant mode
- Update tools_v2 __init__.py to export new node control tools
- Document node control tools in README with key features and implementation status
- Update last modified date in documentation
The new tools provide complete node lifecycle control for automated lab workflows, including stopping nodes for shutdown and suspending nodes while preserving state.
- Remove redundant "(REQUIRED)" and detailed explanations from context_limit field descriptions
- Shorten copilot_mode descriptions by removing parenthetical details about mode capabilities
- Maintain field formatting consistency across LLMModelConfigData, Create, and Update schemas
- Add exception for .claude/development.md to allow tracking development documentation
- Comment out PROJECT_CONTEXT.md exclusion to enable version control of project context file
- Create PROJECT_CONTEXT.md with comprehensive project overview for AI assistant support
- Document project structure, AI copilot data flow, SSE event types, and code standards
- Provide flake8 static analysis guidelines and common error fixes
Fixed tool output serialization in tool_node function to ensure
ToolMessage.content is always in JSON format, not Python str() representation.
This fixes the issue where conversation history showed tool outputs
with single quotes (Python format) instead of standard JSON.
Changes:
- Added json import to gns3_copilot.py
- Modified tool_node() to serialize observation to JSON before creating ToolMessage
- Ensures both SSE streaming and history storage use consistent JSON format
Root cause: ToolMessage was created with raw dict/list objects, which
LangChain converted to Python str() representation when saving to history.
Co-Authored-By: YueGuobin <yueguobin@outlook.com>
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
Rename base_prompt.py to teaching_assistant_prompt.py and lab_assistant_prompt.py to lab_automation_assistant_prompt.py to better reflect their purposes. Update all imports and references accordingly to maintain consistency across the codebase. This improves code readability and aligns naming with the actual functionality of each prompt module.
- Import filter_forbidden_commands utility from command_filter module
- Add _filter_forbidden_commands_from_device_configs method to filter out restricted commands before execution
- Store blocked commands information and log filtered commands for audit purposes
- Update _process_task_results to include blocked commands info in response
- Prevent execution of potentially dangerous commands while maintaining transparency about filtered content
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
- Rename "teaching" mode to "teaching_assistant" for better clarity
- Rename "lab_assistant" mode to "lab_automation_assistant" to reflect expanded capabilities
- Implement mode-specific tool sets: teaching_assistant gets read-only diagnostic tools only, while lab_automation_assistant gets full diagnostic and configuration tools
- Update API documentation examples to reflect new mode names
- Maintain backward compatibility with default tool set initialization
Set logging level for nornir.core and nornir loggers to WARNING in config_tools_nornir.py and display_tools_nornir.py. This prevents nornir from logging task execution at INFO level to the console, as the logging={"enabled": False} parameter in InitNornir only disables plugin internal logs.
- 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 `copilot_mode` field to request examples in API documentation
- Update prompt loader to read `copilot_mode` from flattened config structure
- Support both "teaching" and "lab_assistant" modes for different assistant behaviors
- Add `copilot_mode` field to LLM model configs API with "teaching" (diagnostics only) and "lab_assistant" (full configuration access) modes
- Introduce new `ExecuteMultipleDeviceConfigCommands` tool for executing configuration commands on multiple devices
- Include `tags` field in node data structure for enhanced project management
- Update API documentation examples to reflect new `copilot_mode` field and context limit additions
Removed extensive implementation details and configuration examples from the context window management documentation. The document now focuses on core permissions and prohibitions for tool usage, providing a clearer and more concise reference for allowed and forbidden actions. This streamlines the documentation to essential guidelines only.
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
Refactor context preparation to embed topology information directly into the system prompt using a {{topology_info}} placeholder, replacing the previous approach of appending a separate SystemMessage. This consolidates context into a single system message and simplifies token accounting. The logging is updated to reflect the new token breakdown, showing combined system prompt tokens (base + topology) instead of separate components.
The GNS3TopologyTool has been removed from the list of available tools for the agent. This change simplifies the toolset by eliminating a tool that is no longer needed or supported in the current workflow.
Enhanced the context window management system to properly account for tool definition tokens when trimming messages. The key changes include:
- Updated `trim_messages_for_context` function to accept `tool_tokens` parameter
- Modified token budget allocation logic to subtract tool tokens before message trimming
- Added detailed documentation explaining the token budget distribution between messages and tool definitions
- Implemented prioritized trimming strategy that preserves system messages and recent conversation history
- Added boundary case handling for scenarios where system messages or tools exceed available budget
The improvements ensure more accurate context window management by accounting for the ~1000-2000 tokens typically consumed by tool definitions that LangChain automatically includes in LLM requests.
- Add tiktoken as a required dependency for accurate token counting
- Update documentation with installation instructions and token counting strategy
- Improve logging to include tool definition token estimates
- Enhance error handling to fail fast when tiktoken is not available
- Update context manager to use tiktoken's cl100k_base encoding for GPT-4 compatibility
- Add `context_limit` as required field for LLM model configurations
- Add `context_strategy` as optional field with three trimming strategies
- Update API documentation with detailed examples for GPT-4o and Claude 3.5 Sonnet
- Clarify that context limit is specified in K tokens (thousands of tokens)
- Update example payloads to reflect current model versions and new fields
Add `normalize_tool_response` function to standardize tool output formats for consistent frontend display. The function converts various response types (dict, list, string) into a unified structure with success metrics, detailed data arrays, and metadata. This ensures backward compatibility while providing predictable response formats for UI components.
- Remove unused `llm_calls` variable and redundant logging in `should_continue`
- Simplify title generation routing by eliminating unnecessary condition checks
- Improve code clarity and maintainability by focusing on essential flow control
- 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
