add reasoning settings

[iceljc]

PR Type

Enhancement

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Description

• Add reasoning settings configuration for LLM models

• Update OpenAI provider to use configurable reasoning parameters

• Upgrade OpenAI package to version 2.3.0

• Refactor temperature and effort level handling

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Diagram Walkthrough

flowchart LR
  A["LlmModelSetting"] --> B["ReasoningSetting"]
  B --> C["Temperature & EffortLevel"]
  C --> D["ChatCompletionProvider"]
  D --> E["Dynamic Configuration"]

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File Walkthrough

	Relevant files
Enhancement	LlmModelSetting.cs Add reasoning configuration settings                                          src/Infrastructure/BotSharp.Abstraction/MLTasks/Settings/LlmModelSetting.cs Add ReasoningSetting class with temperature and effort level properties Add reasoning settings property to LlmModelSetting Rename LlmCost to LlmCostSetting for consistency +16/-2    ChatCompletionProvider.cs Refactor reasoning parameters handling                                      src/Plugins/BotSharp.Plugin.OpenAI/Providers/Chat/ChatCompletionProvider.cs Remove hardcoded temperature dictionary for reasoning models Implement dynamic reasoning settings from configuration Update reasoning effort level parsing with new default handling Add pragma warning disable for OpenAI001 +15/-19
Dependencies	Directory.Packages.props Update OpenAI package version                                                        Directory.Packages.props Upgrade OpenAI package from 2.2.0-beta.4 to 2.3.0 +1/-1

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[qodo-merge-pro]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Nullability Reasoning is nullable but consumed without null-checks elsewhere could cause NREs if other providers assume it exists; ensure callers handle null or provide defaults. public ReasoningSetting? Reasoning { get; set; } /// <summary> /// Settings for llm cost /// </summary> public LlmCostSetting Cost { get; set; } = new(); public override string ToString() { return $"[{Type}] {Name} {Endpoint}"; } } public class ReasoningSetting { public float Temperature { get; set; } = 1.0f; public string? EffortLevel { get; set; } Parsing Robustness Temperature is parsed from state with float.Parse which can throw with culture-specific decimals; prefer invariant parsing and TryParse with fallback to config defaults. var temperature = float.Parse(state.GetState("temperature", "0.0")); if (settings?.Reasoning != null) { temperature = settings.Reasoning.Temperature; var level = state.GetState("reasoning_effort_level") .IfNullOrEmptyAs(agent?.LlmConfig?.ReasoningEffortLevel ?? string.Empty) .IfNullOrEmptyAs(settings?.Reasoning?.EffortLevel ?? string.Empty); reasoningEffortLevel = ParseReasoningEffortLevel(level); } var maxTokens = int.TryParse(state.GetState("max_tokens"), out var tokens) ? tokens : agent.LlmConfig?.MaxOutputTokens ?? LlmConstant.DEFAULT_MAX_OUTPUT_TOKEN; return new ChatCompletionOptions() { Temperature = temperature, MaxOutputTokenCount = maxTokens, Default Effort ParseReasoningEffortLevel returns null on empty level, changing behavior from previous default; verify downstream expects optional value and define explicit default if required. private ChatReasoningEffortLevel? ParseReasoningEffortLevel(string? level) { if (string.IsNullOrWhiteSpace(level)) { return null; } var effortLevel = new ChatReasoningEffortLevel("minimal"); switch (level.ToLower()) { case "low": effortLevel = ChatReasoningEffortLevel.Low; break; case "medium": effortLevel = ChatReasoningEffortLevel.Medium; break; case "high": effortLevel = ChatReasoningEffortLevel.High;

[qodo-merge-pro]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
High-level	Reasoning config fallback gaps The new reasoning settings are only applied when settings.Reasoning exists, but when absent the code drops both model-specific defaults and the previous default effort level, potentially changing behavior across non-reasoning models. Consider restoring a clear fallback chain (state -> agent -> settings -> constant) for both temperature and reasoning effort, and limit ReasoningEffortLevel to models that support it to avoid unintended nulls or API mismatches. Examples: src/Plugins/BotSharp.Plugin.OpenAI/Providers/Chat/ChatCompletionProvider.cs [490-499] ChatReasoningEffortLevel? reasoningEffortLevel = null; var temperature = float.Parse(state.GetState("temperature", "0.0")); if (settings?.Reasoning != null) { temperature = settings.Reasoning.Temperature; var level = state.GetState("reasoning_effort_level") .IfNullOrEmptyAs(agent?.LlmConfig?.ReasoningEffortLevel ?? string.Empty) .IfNullOrEmptyAs(settings?.Reasoning?.EffortLevel ?? string.Empty); reasoningEffortLevel = ParseReasoningEffortLevel(level); } Solution Walkthrough: Before: ChatReasoningEffortLevel? reasoningEffortLevel = null; var temperature = float.Parse(state.GetState("temperature", "0.0")); if (settings?.Reasoning != null) { temperature = settings.Reasoning.Temperature; var level = state.GetState("reasoning_effort_level") .IfNullOrEmptyAs(agent?.LlmConfig?.ReasoningEffortLevel) .IfNullOrEmptyAs(settings?.Reasoning?.EffortLevel); reasoningEffortLevel = ParseReasoningEffortLevel(level); } // If settings.Reasoning is null, temperature defaults to 0.0 and reasoningEffortLevel is null, // losing previous default behaviors. After: // Restore a full fallback chain outside the conditional check var temperature = float.Parse(state.GetState("temperature", "0.0")); if (settings?.Reasoning != null) { temperature = settings.Reasoning.Temperature; } var level = state.GetState("reasoning_effort_level") .IfNullOrEmptyAs(agent?.LlmConfig?.ReasoningEffortLevel) .IfNullOrEmptyAs(settings?.Reasoning?.EffortLevel) .IfNullOrEmptyAs(LlmConstant.DEFAULT_REASONING_EFFORT_LEVEL); // Only parse effort level for models that support it var reasoningEffortLevel = (settings?.Reasoning != null) ? ParseReasoningEffortLevel(level) : null; Suggestion importance[1-10]: 9 __ Why: The suggestion correctly identifies a significant regression where the removal of fallback logic for temperature and effort level can silently alter model behavior, which is a critical design flaw.	High
Possible issue	Make temperature parsing resilient Avoid unguarded float.Parse which can throw on invalid or locale-formatted values. Use float.TryParse with InvariantCulture and fall back to a safe default or configured temperature. This prevents runtime exceptions from corrupt state inputs. src/Plugins/BotSharp.Plugin.OpenAI/Providers/Chat/ChatCompletionProvider.cs [491-499] -var temperature = float.Parse(state.GetState("temperature", "0.0")); +var temperatureStr = state.GetState("temperature", "0.0"); +if (!float.TryParse(temperatureStr, System.Globalization.NumberStyles.Float, System.Globalization.CultureInfo.InvariantCulture, out var temperature)) +{ + temperature = 0.0f; +} if (settings?.Reasoning != null) { temperature = settings.Reasoning.Temperature; var level = state.GetState("reasoning_effort_level") .IfNullOrEmptyAs(agent?.LlmConfig?.ReasoningEffortLevel ?? string.Empty) .IfNullOrEmptyAs(settings?.Reasoning?.EffortLevel ?? string.Empty); reasoningEffortLevel = ParseReasoningEffortLevel(level); } Apply / Chat Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies that float.Parse can throw an exception and proposes using float.TryParse for robustness, which is a valid improvement for error handling.	Medium
	Validate max tokens bounds Ensure negative or zero values for max tokens are not passed through, as they can cause API errors. Clamp to a minimum of 1 and optionally cap to a sensible upper bound from configuration. src/Plugins/BotSharp.Plugin.OpenAI/Providers/Chat/ChatCompletionProvider.cs [501-503] var maxTokens = int.TryParse(state.GetState("max_tokens"), out var tokens) ? tokens : agent.LlmConfig?.MaxOutputTokens ?? LlmConstant.DEFAULT_MAX_OUTPUT_TOKEN; +if (maxTokens <= 0) +{ + maxTokens = agent.LlmConfig?.MaxOutputTokens ?? LlmConstant.DEFAULT_MAX_OUTPUT_TOKEN; +} Apply / Chat Suggestion importance[1-10]: 7 __ Why: This suggestion correctly points out that maxTokens could be an invalid non-positive value, and adding a check prevents potential downstream API errors, improving input validation.	Medium
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