7b459b8ca1
将 4 个压缩参数（threshold_ratio、llm_compaction_threshold_ratio、 truncate_max_tokens、preserve_count）从硬编码提取到 config.json 顶层 compaction 节，前端新增 CompactionTab 供用户调整。 后端： - config: 新增 CompactionConfig 结构体，Config 新增 compaction 字段 - context_compressor: 4 个参数内聚到 ContextCompressor 实例字段； 新增 with_compaction_config() 构造函数和 truncate_tool_results() 方法； 对用户配置做防御性 clamp（ratio ∈ [0.1,1.0]，整数 ≥ 1） - agent_loop: 调用 compressor.truncate_tool_results() 实现零参数耦合 - agent_factory: 新增 build_compressor() 方法，in-loop 与 sync 兜底 两条压缩路径共用同一套用户配置 - session: with_factories 改用 agent_factory.build_compressor()，修复 sync 压缩路径忽略用户配置的 P0 问题 前端： - types: 新增 CompactionConfig 接口，TabId 新增 'compaction' - 新建 CompactionTab.tsx，4 个 number input 分两组 SectionCard - constants + ConfigPage: 注册"上下文压缩" Tab（Archive 图标） 清理：删除无调用方的 from_provider_config/from_runtime_config/with_config 方法及 DEFAULT_THRESHOLD_RATIO/LLM_COMPACTION_THRESHOLD_RATIO 常量。 测试：context_compressor 18 + session 31 + agent_loop 42 全通过。
279 lines
12 KiB
Rust
279 lines
12 KiB
Rust
use std::sync::Arc;
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use crate::agent::context_compressor::ContextCompressor;
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use crate::agent::{AgentError, AgentLoop, AgentRuntimeConfig, CompositeSystemPromptProvider, SystemPromptProvider};
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use crate::config::{CompactionConfig, LLMProviderConfig, ModelResolver};
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use crate::domain::CapabilityPolicy;
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use crate::experts::ExpertPromptProvider;
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use crate::experts::ExpertRuntime;
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use crate::gateway::agent_prompt_provider::AgentPromptProvider;
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use crate::gateway::model_selection::ModelSelectionStore;
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use crate::gateway::tool_prompt_provider::ToolPromptProvider;
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use crate::skills::{SkillPromptProvider, SkillRuntime};
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use crate::storage::PromptInjectionRepository;
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use crate::storage::persistent_session_id;
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use crate::tools::task::runtime::{SubagentPromptProvider, SubagentRuntime};
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use crate::tools::{ToolContext, ToolRegistry};
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/// 构建与 Agent 实际使用的完全一致的组合系统提示词 Provider。
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///
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/// 单一来源：AgentFactory::create 与命令侧（/save、/save-session、/current）
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/// 都调用此函数，确保保存到文件的系统提示词与 LLM 实际接收的提示词一致。
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///
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/// Provider 顺序：AgentPrompt → SkillPrompt → ExpertPrompt → SubagentPrompt → TodoPrompt
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pub(crate) fn build_system_prompt_provider(
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reinject_every: usize,
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provider_config: LLMProviderConfig,
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prompt_repository: Arc<dyn PromptInjectionRepository>,
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skills: Arc<SkillRuntime>,
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experts: Arc<ExpertRuntime>,
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subagent_runtime: Arc<SubagentRuntime>,
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) -> Arc<dyn SystemPromptProvider> {
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Arc::new(CompositeSystemPromptProvider::new(vec![
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Box::new(AgentPromptProvider::new(
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reinject_every,
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provider_config,
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prompt_repository,
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)),
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Box::new(SkillPromptProvider::new(skills, experts.clone())),
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Box::new(ExpertPromptProvider::new(experts.clone())),
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Box::new(SubagentPromptProvider::new(subagent_runtime, experts)),
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Box::new(ToolPromptProvider::new()),
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]))
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}
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#[derive(Clone)]
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pub(crate) struct AgentFactory {
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tools: Arc<ToolRegistry>,
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skills: Arc<SkillRuntime>,
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experts: Arc<ExpertRuntime>,
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subagent_runtime: Arc<SubagentRuntime>,
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reinject_every: usize,
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prompt_repository: Arc<dyn PromptInjectionRepository>,
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/// Provider/Model 解析器：按专家 frontmatter 中的 provider/model 字段覆盖基础配置
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model_resolver: Arc<ModelResolver>,
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/// per-session 的用户模型选择（最高优先级，覆盖专家配置）
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model_selections: Arc<ModelSelectionStore>,
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/// 上下文压缩算法配置（所有 agent 共享）
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compaction_config: CompactionConfig,
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/// 实例创建时间戳（用于区分新旧 AgentFactory 实例）
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instance_id: u64,
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}
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pub(crate) struct AgentBuildRequest<'a> {
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pub(crate) channel_name: &'a str,
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pub(crate) session_chat_id: &'a str,
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pub(crate) notification_chat_id: Option<&'a str>,
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pub(crate) sender_id: Option<&'a str>,
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pub(crate) message_id: Option<&'a str>,
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pub(crate) provider_config: LLMProviderConfig,
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/// 当前话题 ID（可选）：用于 todo 等按 topic 隔离的工具
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pub(crate) topic_id: Option<String>,
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/// 取消信号接收端（可选）：Agent 在每次迭代时检查是否被取消
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pub(crate) cancel_token: Option<tokio::sync::watch::Receiver<()>>,
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}
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impl AgentFactory {
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pub(crate) fn new(
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tools: Arc<ToolRegistry>,
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skills: Arc<SkillRuntime>,
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experts: Arc<ExpertRuntime>,
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subagent_runtime: Arc<SubagentRuntime>,
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reinject_every: usize,
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prompt_repository: Arc<dyn PromptInjectionRepository>,
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model_resolver: Arc<ModelResolver>,
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model_selections: Arc<ModelSelectionStore>,
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compaction_config: CompactionConfig,
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) -> Self {
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// 使用 Arc 指针地址作为实例标识符，用于区分新旧 AgentFactory 实例
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let instance_id = Arc::as_ptr(&tools) as u64;
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tracing::info!(
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instance_id = instance_id,
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tool_count = tools.tool_names().len(),
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"AgentFactory::new created"
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);
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Self {
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tools,
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skills,
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experts,
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subagent_runtime,
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reinject_every,
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prompt_repository,
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model_resolver,
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model_selections,
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compaction_config,
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instance_id,
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}
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}
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/// 构造 ContextCompressor（参数内聚到 ContextCompressor，CompactionConfig 注入）。
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/// AgentLoop（in-loop 压缩）和 Session（sync 兜底压缩）共用此方法，
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/// 确保两条压缩路径使用同一套用户配置的压缩参数。
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pub(crate) fn build_compressor(&self, runtime_config: &AgentRuntimeConfig) -> ContextCompressor {
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ContextCompressor::with_compaction_config(
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runtime_config.context_window_tokens,
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runtime_config.context_summary_char_budget,
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&self.compaction_config,
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)
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}
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pub(crate) fn create(&self, request: AgentBuildRequest<'_>) -> Result<AgentLoop, AgentError> {
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let session_id = persistent_session_id(request.channel_name, request.session_chat_id);
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// 读取所选专家（用于工具过滤 + 子代理策略 + 模型覆盖）
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let expert = self.experts.selected_expert_for(&session_id);
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let expert_capability = expert.as_ref().map(|e| e.capability.clone());
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// 按专家 frontmatter 中的 provider/model 字段解析覆盖基础 provider_config。
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// 引用不存在的 provider/model 名时报错并阻止会话（用户主动选择的角色，配置错误应明确反馈）。
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let expert_provider_config = match &expert {
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Some(e) if e.provider.is_some() || e.model.is_some() => {
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let resolved = self
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.model_resolver
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.resolve(
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e.provider.as_deref(),
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e.model.as_deref(),
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&request.provider_config,
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)
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.map_err(|e| AgentError::Other(e.to_string()))?;
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tracing::info!(
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instance_id = self.instance_id,
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session_id = %session_id,
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expert = %e.name,
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provider = %resolved.name,
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model_id = %resolved.model_id,
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"AgentFactory: applied expert model override"
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);
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resolved
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}
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_ => request.provider_config.clone(),
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};
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// 按用户手动选择的 provider/model 覆盖（最高优先级，覆盖专家配置）。
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// 引用不存在的 provider/model 名时报错并阻止会话（用户主动选择，配置错误应明确反馈）。
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let effective_provider_config = match self.model_selections.get(&session_id) {
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Some((user_provider, user_model))
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if user_provider.is_some() || user_model.is_some() =>
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{
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let resolved = self
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.model_resolver
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.resolve(
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user_provider.as_deref(),
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user_model.as_deref(),
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&expert_provider_config,
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)
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.map_err(|e| AgentError::Other(e.to_string()))?;
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tracing::info!(
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instance_id = self.instance_id,
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session_id = %session_id,
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provider = %resolved.name,
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model_id = %resolved.model_id,
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"AgentFactory: applied user model override"
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);
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resolved
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}
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_ => expert_provider_config,
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};
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// 诊断日志：记录 agent 实际使用的配置和实例 ID
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tracing::info!(
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instance_id = self.instance_id,
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channel = %request.channel_name,
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session_id = %session_id,
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provider = %effective_provider_config.name,
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model_id = %effective_provider_config.model_id,
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tool_count = self.tools.tool_names().len(),
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"AgentFactory: creating agent with config"
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);
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// 创建组合的系统提示词提供者（与命令侧 /save 等共享同一构建逻辑）
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let system_prompt_provider = build_system_prompt_provider(
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self.reinject_every,
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effective_provider_config.clone(),
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self.prompt_repository.clone(),
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self.skills.clone(),
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self.experts.clone(),
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self.subagent_runtime.clone(),
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);
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// 按所选专家的工具策略过滤工具集（含内置 + MCP 工具）。
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// 无专家或专家未声明工具策略时，复用共享的 Arc<ToolRegistry>（零拷贝）。
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let base_tool_count = self.tools.tool_names().len();
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let tools: Arc<ToolRegistry> = match &expert_capability {
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Some(cap) if cap.has_tool_policy() => {
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let filtered = self.build_filtered_registry(cap);
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let filtered_count = filtered.tool_names().len();
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tracing::info!(
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instance_id = self.instance_id,
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session_id = %session_id,
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base_tool_count,
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filtered_tool_count = filtered_count,
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"AgentFactory: applied expert tool policy"
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);
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Arc::new(filtered)
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}
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_ => self.tools.clone(),
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};
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AgentLoop::with_tools_and_system_prompt_provider(
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effective_provider_config.clone(),
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tools,
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system_prompt_provider,
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Some(self.skills.clone()),
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)
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.map(|agent| {
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// notification_chat_id 优先，否则使用 session_chat_id
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let tool_chat_id = request
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.notification_chat_id
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.unwrap_or(request.session_chat_id);
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// 构建上下文压缩器（参数内聚到 ContextCompressor，CompactionConfig 注入）
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let runtime_config = AgentRuntimeConfig::from(effective_provider_config.clone());
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let compressor = Arc::new(self.build_compressor(&runtime_config));
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let mut agent = agent
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.with_tool_context(ToolContext {
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channel_name: Some(request.channel_name.to_string()),
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sender_id: request.sender_id.map(str::to_string),
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chat_id: Some(tool_chat_id.to_string()),
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session_id: Some(session_id),
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topic_id: request.topic_id.clone(),
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message_id: request.message_id.map(str::to_string),
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message_seq: None,
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subagent_description: None,
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nesting_depth: 0,
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task_id: None,
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parent_task_id: None,
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tool_call_id: None,
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// 注入专家 capability，TaskTool 据此强制校验子代理白/黑名单
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parent_capability: expert_capability.clone(),
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})
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.with_compressor(Some(compressor));
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// 如果有取消信号接收端，注入 Agent
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if let Some(token) = request.cancel_token {
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agent = agent.with_cancel_token(token);
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}
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agent
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})
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}
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/// 按专家 CapabilityPolicy 构建过滤后的 ToolRegistry 副本。
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/// 生效顺序：先白名单取交集，再黑名单扣除（与 subagent filter_tool_registry 语义一致）。
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/// 底层工具为 Arc<dyn ToolTrait>，克隆廉价。
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fn build_filtered_registry(&self, policy: &CapabilityPolicy) -> ToolRegistry {
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// 1. 白名单（取交集）；None 表示不限，复制一份以便后续黑名单过滤
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let after_allow: ToolRegistry = match &policy.allowed_tools {
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Some(allowed) => {
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let refs: Vec<&str> = allowed.iter().map(|s| s.as_str()).collect();
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self.tools.only(&refs)
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}
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None => self.tools.without(&[]),
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};
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// 2. 黑名单（扣除）
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if policy.denied_tools.is_empty() {
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after_allow
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} else {
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let refs: Vec<&str> = policy.denied_tools.iter().map(|s| s.as_str()).collect();
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after_allow.without(&refs)
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}
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}
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}
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