mirror of
https://github.com/GNS3/gns3-server.git
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e68cc17ed5
## 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
259 lines
8.3 KiB
Python
259 lines
8.3 KiB
Python
# SPDX-License-Identifier: GPL-3.0-or-later
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#
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# GNS3-Copilot - AI-powered Network Lab Assistant for GNS3
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#
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# This file is part of GNS3-Copilot project.
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#
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# GNS3-Copilot is free software: you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation, either version 3 of the License, or (at your
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# option) any later version.
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#
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# GNS3-Copilot is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
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# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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# for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with GNS3-Copilot. If not, see <https://www.gnu.org/licenses/>.
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#
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# Copyright (C) 2025 Yue Guobin (岳国宾)
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# Author: Yue Guobin (岳国宾)
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#
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# Project Home: https://github.com/yueguobin/gns3-copilot
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#
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"""
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Model Factory for GNS3-Copilot Agent
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This module provides factory functions to create fresh LLM model instances.
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Configuration is passed directly from the database.
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"""
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import logging
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from typing import Any
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from typing import Optional
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from langchain.chat_models import init_chat_model
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logger = logging.getLogger(__name__)
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def _load_llm_config(
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llm_config: Optional[dict[str, Any]] = None,
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) -> dict[str, str]:
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"""
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Convert llm_config dict to model factory format.
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Args:
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llm_config: Configuration dictionary from database
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Returns:
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Dictionary containing model configuration.
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Raises:
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ValueError: If configuration is missing or invalid.
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"""
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if not llm_config:
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raise ValueError("LLM configuration is required")
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logger.info(
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"Using LLM config: provider=%s, model=%s",
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llm_config.get("provider"),
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llm_config.get("model"),
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)
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return {
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"model_name": llm_config.get("model", ""),
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"model_provider": llm_config.get("provider", ""),
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"api_key": llm_config.get("api_key", ""),
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"base_url": llm_config.get("base_url", ""),
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"temperature": str(llm_config.get("temperature", "0")),
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}
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def create_base_model(
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llm_config: Optional[dict[str, Any]] = None,
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) -> Any:
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"""
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Create a fresh base LLM model instance.
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Args:
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llm_config: Configuration dictionary from database
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Returns:
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Any: A new LLM model instance configured with current settings.
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The actual type depends on the provider (e.g., ChatOpenAI, etc.).
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Raises:
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ValueError: If required configuration fields are missing or invalid.
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RuntimeError: If model creation fails.
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"""
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config_vars = _load_llm_config(llm_config)
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# Log the loaded configuration (mask sensitive data)
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logger.info(
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"Creating base model: name=%s, provider=%s, base_url=%s, "
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"temperature=%s",
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config_vars["model_name"],
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config_vars["model_provider"],
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config_vars["base_url"] if config_vars["base_url"] else "default",
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config_vars["temperature"],
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)
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# Validate required fields
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if not config_vars["model_name"]:
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raise ValueError("LLM configuration requires 'model' field")
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if not config_vars["model_provider"]:
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raise ValueError("LLM configuration requires 'provider' field")
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try:
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# Prepare parameters for init_chat_model
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init_params = {
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"model": config_vars["model_name"],
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"model_provider": config_vars["model_provider"],
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"api_key": config_vars["api_key"],
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"base_url": config_vars["base_url"],
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"temperature": config_vars["temperature"],
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"configurable_fields": "any",
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"config_prefix": "foo",
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}
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# Disable DeepSeek thinking mode to avoid reasoning_content handling issues
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# DeepSeek models enable thinking mode by default, which returns reasoning_content
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# that must be passed back to the API in subsequent requests. To simplify
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# message handling and avoid 400 errors, we disable it here.
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if config_vars["model_provider"] == "deepseek":
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init_params["extra_body"] = {"thinking": {"type": "disabled"}}
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logger.debug("DeepSeek thinking mode disabled")
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model = init_chat_model(**init_params)
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logger.debug("Base model created successfully")
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return model
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except Exception as e:
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logger.error("Failed to create base model: %s", e)
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raise RuntimeError(f"Failed to create base model: {e}") from e
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def create_title_model(
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llm_config: Optional[dict[str, Any]] = None,
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) -> Any:
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"""
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Create a fresh title generation model instance.
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This creates a model instance suitable for generating conversation titles.
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It uses the same configuration as the base model but with a higher
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temperature for more creative output.
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Args:
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llm_config: Configuration dictionary from database
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Returns:
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Any: A new LLM model instance for title generation.
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The actual type depends on the provider.
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Raises:
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ValueError: If required configuration fields are missing or invalid.
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RuntimeError: If model creation fails.
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"""
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config_vars = _load_llm_config(llm_config)
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logger.info(
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"Creating title model: name=%s, provider=%s, base_url=%s, "
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"temperature=1.0",
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config_vars["model_name"],
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config_vars["model_provider"],
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config_vars["base_url"] if config_vars["base_url"] else "default",
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)
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# Validate required fields
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if not config_vars["model_name"]:
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raise ValueError("LLM configuration requires 'model' field")
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if not config_vars["model_provider"]:
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raise ValueError("LLM configuration requires 'provider' field")
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try:
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# Prepare parameters for init_chat_model
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init_params = {
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"model": config_vars["model_name"],
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"model_provider": config_vars["model_provider"],
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"api_key": config_vars["api_key"],
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"base_url": config_vars["base_url"],
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"temperature": "1.0", # Higher temperature for more creative titles
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"configurable_fields": "any",
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"config_prefix": "foo",
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}
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# Disable DeepSeek thinking mode to avoid reasoning_content handling issues
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# DeepSeek models enable thinking mode by default, which returns reasoning_content
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# that must be passed back to the API in subsequent requests. To simplify
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# message handling and avoid 400 errors, we disable it here.
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if config_vars["model_provider"] == "deepseek":
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init_params["extra_body"] = {"thinking": {"type": "disabled"}}
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logger.debug("DeepSeek thinking mode disabled for title model")
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model = init_chat_model(**init_params)
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logger.debug("Title model created successfully")
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return model
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except Exception as e:
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logger.error("Failed to create title model: %s", e)
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raise RuntimeError(f"Failed to create title model: {e}") from e
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def create_model_with_tools(
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model: Any,
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tools: list[Any],
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) -> Any:
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"""
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Bind tools to a model instance.
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Args:
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model: The base model instance.
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tools: List of tools to bind to the model.
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Returns:
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Any: A model instance with tools bound (type varies by provider).
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Raises:
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RuntimeError: If tool binding fails.
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"""
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try:
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model_with_tools = model.bind_tools(tools)
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logger.debug("Model bound with %d tools successfully", len(tools))
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return model_with_tools
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except Exception as e:
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logger.error("Failed to bind tools to model: %s", e)
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raise RuntimeError(f"Failed to bind tools to model: {e}") from e
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def create_base_model_with_tools(
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tools: list[Any],
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llm_config: Optional[dict[str, Any]] = None,
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) -> Any:
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"""
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Create a fresh base model instance with tools bound.
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This is a convenience function that combines creating the base model
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and binding tools to it.
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Args:
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tools: List of tools to bind to the model.
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llm_config: Configuration dictionary from database
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Returns:
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Any: A new model instance with tools bound (type varies by provider).
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Raises:
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ValueError: If required configuration fields are missing.
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RuntimeError: If model creation or tool binding fails.
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"""
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base_model = create_base_model(llm_config)
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return create_model_with_tools(base_model, tools)
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