## Summary
Add a complete fault injection system for GNS3 Copilot, migrate all
skills from local Python files to an external Git repository with
hot reload support, and restructure Copilot API under /copilot/.
## Key Changes
### Fault Injection
- New troubleshooting_injection mode with InjectionSkillsTool
- 368 fault scenarios across 39 protocol categories
- Context-based filtering (LLM must pass topology protocols)
### External Skills Repository
- SkillsManager: Git clone/pull, version tracking, smart updates
- SkillsLoader: YAML skills + Markdown prompts from external repo
- Hot reload via POST /copilot/reload/skills
- Configurable via gns3_server.conf
### Architecture
- API unified under /copilot/ prefix
- SkillsManager moved from Controller to agent module
- Lazy initialization with startup background preload
- Per-command Git timeout, smart update checks
- Forbidden commands hot-reloadable from external repo
- 32 INFO logs downgraded to DEBUG
DeepSeek models (deepseek-v4-flash/pro) enable thinking mode by default,
which returns reasoning_content that must be passed back to the API in
subsequent requests. This causes 400 errors in multi-turn conversations
when the reasoning_content is not properly handled.
This commit disables thinking mode by passing extra_body={"thinking": {"type": "disabled"}}
as an explicit parameter to DeepSeek models, preventing the reasoning_content
field from being generated.
Modified:
- create_base_model(): Add extra_body parameter with thinking mode disabled
- create_title_model(): Add extra_body parameter with thinking mode disabled
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.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.
- 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)
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
- 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.
Remove support for environment variable fallback in LLM model configuration. The configuration now strictly follows:
1. Provided llm_config dictionary (highest priority)
2. Fetch from llm_model_configs system via connector_factory (requires user_id and jwt_token)
This change ensures consistent configuration management and eliminates the outdated environment variable approach. When no configuration is found, a clear ValueError is raised with appropriate error messages.
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.
Update all references from FlowNet-Lab to GNS3-Copilot in package names, documentation, and logging. This includes:
- Module and package __init__.py files
- License headers and file descriptions
- Log messages and internal comments
- Remove deprecated tools: GNS3CreateAreaDrawingTool and LinuxTelnetBatchTool
The renaming aligns with the project's new branding while maintaining all existing functionality.
Refactor model factory to support three configuration sources in priority order:
1. Direct llm_config dictionary parameter
2. Fetch from llm_model_configs system via connector_factory (requires user_id and jwt_token)
3. Environment variables as fallback for backward compatibility
This improves flexibility by allowing runtime configuration while maintaining compatibility with existing environment-based setups.
- Remove direct logging of LLM config from gns3_copilot.py
- Update model_factory to accept configuration from llm_model_configs dictionary
- Add fallback to environment variables for backward compatibility
- Centralize configuration loading in _load_llm_config function
Integrate the gns3-copilot AI assistant module to provide intelligent
automation and interaction capabilities for GNS3 network emulation.
Key components:
- AI agent framework with LLM integration (supports Qwen vision model)
- GNS3 client library for project topology management
- Extensive prompt templates for various network operation scenarios
- Tool library for node creation, linking, configuration, and management
- Support for English level assessment (A1-C2) and specialized personas
- Network drawing and topology visualization tools
- Linux device automation via Nornir/Telnetlib
- Window controller for UI interaction
Features:
- Multi-modal AI agent with vision capabilities
- Automated network topology deployment and configuration
- Interactive node and drawing management
- File-based project operations (read, write, list)
- Specialized prompts for different scenarios and skill levels
- Comprehensive tool set for network device management
