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
- Move command-security.md and hitl-implementation-plan.md to todo/ directory
- Add new documentation for orphan tool calls recovery with detailed problem analysis and implementation plan
- Document scenarios causing orphan tool calls and propose fix strategy using LangGraph API
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
- 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 `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.
- 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
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
- Move AI chat API design document to gns3-copilot directory
- Add comprehensive HITL implementation plan with architecture and workflow
- Introduce human-in-the-loop confirmation for dangerous configuration commands
- Include state management, API endpoints, and frontend integration details
- Provide testing plan and deployment steps for the new feature
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.
- 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.
This commit introduces a comprehensive design document outlining the implementation of AI Chat API for GNS3 Copilot Agent within GNS3 Server. The document provides:
- Overview and background on existing components including GNS3 Copilot Agent, LLM configuration management, and API framework
- Reference implementation details from FlowNet-Lab project
- Architecture design with clear component interactions between frontend clients and backend services
- RESTful API specifications for chat streaming, session management, and history retrieval
- Implementation details covering project-based agent management, SQLite checkpoint storage, and LangGraph integration
- Security considerations and deployment guidelines
The design enables clients to interact with GNS3 Copilot Agent through standardized APIs, supporting real-time chat streaming and persistent conversation sessions per project.
Updated documentation and implementation to clearly define the priority order for selecting default LLM model configurations. The logic now explicitly states:
1. User's config marked with `is_default: true` (highest priority)
2. Group's config marked with `is_default: true`
3. First config in the list (user configs come before group configs)
This ensures consistent behavior between the API documentation and the actual implementation in the repository code.
Update API documentation to reflect new nested `config` object structure in LLM model configurations endpoints. The response format now encapsulates provider-specific fields (provider, base_url, model, temperature, api_key, max_tokens) within a `config` object, while moving ownership metadata (user_id, group_id, is_default, version, created_at, updated_at) to the top level. This aligns the user-facing endpoints with the group configuration structure and improves API consistency.
- Add note clarifying that GET endpoints for groups return the same structure as user endpoints
- Document LLMModelConfigListResponse schema with default configuration selection logic
- Add comprehensive example for GET group configurations endpoint
- Update endpoint numbering to accommodate new group endpoints
- Ensure consistency between user and group API documentation
- Update API documentation to explicitly describe default configuration selection logic
- Add fallback behavior in repository to use first config when no default is marked
- Clarify difference between `/default` endpoint and `default_config` field
- Document that `default_config` is never null when configs list is not empty
- Add `/default` endpoints for users and groups to retrieve default LLM model configurations
- Update documentation to clarify that users receive both own and inherited configurations
- Improve response examples to show combined configs with source field
- Add 404 response example for missing default configurations
- Fix optimistic locking documentation formatting
- Add `model_type` field to database schema with supported values (text, vision, stt, tts, multimodal, embedding, reranking, other)
- Add `name` field as table-level column for indexing and filtering
- Add reserved JSONB fields for future extensibility
- Update API request/response schemas to include `model_type` and `name` fields
- Add new `LLMModelConfigWithSource` schema for detailed configuration responses
- Update usage examples to reflect new required fields
- Improve database constraints and indexing documentation
Add comprehensive documentation for optimistic locking implementation in the LLM model configurations API. The update includes:
- Added "Optimistic Locking" feature to the overview section
- Added `version` field to the database schema table
- Updated `LLMModelConfigUpdate` schema to include `expected_version` parameter
- Updated `LLMModelConfigResponse` schema to include `version` field
- Added detailed examples for update operations with optimistic locking
- Included conflict handling workflow and client retry instructions
This documentation ensures users understand how to prevent concurrent modification conflicts when updating LLM model configurations.
- Introduce new API route `/access` for managing LLM model configurations
- Add LLMModelConfig model to database models
- Include comprehensive schemas for LLM model config CRUD operations
- Register new router with tags for LLM Model Configurations
