diff --git a/desktop/src-tauri/src/intelligence_hooks.rs b/desktop/src-tauri/src/intelligence_hooks.rs index 7b6b057..0109644 100644 --- a/desktop/src-tauri/src/intelligence_hooks.rs +++ b/desktop/src-tauri/src/intelligence_hooks.rs @@ -7,8 +7,10 @@ use tracing::{debug, warn}; +use std::collections::HashMap; use std::sync::Arc; use tauri::Emitter; +use tokio::sync::RwLock; use zclaw_growth::VikingStorage; use crate::intelligence::identity::IdentityManagerState; @@ -16,6 +18,36 @@ use crate::intelligence::heartbeat::HeartbeatEngineState; use crate::intelligence::reflection::{MemoryEntryForAnalysis, ReflectionEngineState}; use zclaw_runtime::driver::LlmDriver; +// --------------------------------------------------------------------------- +// Identity prompt cache — avoids mutex + disk I/O on every request +// --------------------------------------------------------------------------- + +struct CachedIdentity { + prompt: String, + #[allow(dead_code)] // Reserved for future TTL-based cache validation + soul_hash: u64, +} + +static IDENTITY_CACHE: std::sync::LazyLock>> = + std::sync::LazyLock::new(|| RwLock::new(HashMap::new())); + +/// Invalidate cached identity prompt for a given agent (call when soul.md changes). +pub fn invalidate_identity_cache(agent_id: &str) { + let cache = &*IDENTITY_CACHE; + // Non-blocking: spawn a task to remove the entry + if let Ok(mut guard) = cache.try_write() { + guard.remove(agent_id); + } +} + +/// Simple hash for cache invalidation — uses string content hash. +fn content_hash(s: &str) -> u64 { + use std::hash::{Hash, Hasher}; + let mut hasher = std::collections::hash_map::DefaultHasher::new(); + s.hash(&mut hasher); + hasher.finish() +} + /// Run pre-conversation intelligence hooks /// /// Builds identity-enhanced system prompt (SOUL.md + instructions) and @@ -29,10 +61,29 @@ pub async fn pre_conversation_hook( _user_message: &str, identity_state: &IdentityManagerState, ) -> Result { - // Build identity-enhanced system prompt (SOUL.md + instructions) - // Memory context is injected by MemoryMiddleware in the kernel middleware chain, - // not here, to avoid duplicate injection. - let enhanced_prompt = match build_identity_prompt(agent_id, "", identity_state).await { + // Check identity prompt cache first (avoids mutex + disk I/O) + let cache = &*IDENTITY_CACHE; + { + let guard = cache.read().await; + if let Some(cached) = guard.get(agent_id) { + // Cache hit — still need continuity context, but skip identity build + let continuity_context = build_continuity_context(agent_id, _user_message).await; + let mut result = cached.prompt.clone(); + if !continuity_context.is_empty() { + result.push_str(&continuity_context); + } + debug!("[intelligence_hooks] Identity cache HIT for agent {}", agent_id); + return Ok(result); + } + } + + // Cache miss — build identity prompt and continuity context in parallel + let (identity_result, continuity_context) = tokio::join!( + build_identity_prompt_cached(agent_id, "", identity_state, cache), + build_continuity_context(agent_id, _user_message) + ); + + let enhanced_prompt = match identity_result { Ok(prompt) => prompt, Err(e) => { warn!( @@ -43,9 +94,6 @@ pub async fn pre_conversation_hook( } }; - // Cross-session continuity: check for unresolved pain points and recent experiences - let continuity_context = build_continuity_context(agent_id, _user_message).await; - let mut result = enhanced_prompt; if !continuity_context.is_empty() { result.push_str(&continuity_context); @@ -240,6 +288,8 @@ pub async fn post_conversation_hook( warn!("[intelligence_hooks] Failed to update soul with agent name: {}", e); } else { debug!("[intelligence_hooks] Updated agent name to '{}' in soul", name); + // Invalidate cache since soul.md changed + invalidate_identity_cache(agent_id); } } drop(manager); @@ -340,21 +390,34 @@ async fn build_memory_context( Ok(context) } -/// Build identity-enhanced system prompt -async fn build_identity_prompt( +/// Build identity-enhanced system prompt and cache the result. +async fn build_identity_prompt_cached( agent_id: &str, memory_context: &str, identity_state: &IdentityManagerState, + cache: &RwLock>, ) -> Result { - // IdentityManagerState is Arc> - // tokio::sync::Mutex::lock() returns MutexGuard directly let mut manager = identity_state.lock().await; + // Read current soul content for hashing + let soul_content = manager.get_file(agent_id, crate::intelligence::identity::IdentityFile::Soul); + let soul_hash = content_hash(&soul_content); + let prompt = manager.build_system_prompt( agent_id, if memory_context.is_empty() { None } else { Some(memory_context) }, ).await; + // Cache the result + drop(manager); // Release lock before acquiring write guard + { + let mut guard = cache.write().await; + guard.insert(agent_id.to_string(), CachedIdentity { + prompt: prompt.clone(), + soul_hash, + }); + } + Ok(prompt) } diff --git a/desktop/src/lib/llm-service.ts b/desktop/src/lib/llm-service.ts index 7888087..ec5ca6e 100644 --- a/desktop/src/lib/llm-service.ts +++ b/desktop/src/lib/llm-service.ts @@ -646,22 +646,24 @@ const HARDCODED_PROMPTS: Record }, suggestions: { - system: `你是对话分析助手和智能管家。根据对话内容和用户画像信息，生成 3 个个性化建议。 + system: `你是 ZCLAW 的管家助手，需要站在用户角度思考他们真正需要什么，生成 3 个个性化建议。 ## 生成规则 -1. 2 条对话续问（深入当前话题，帮助用户继续探索） -2. 1 条管家关怀（基于用户消息中提供的痛点、经验或技能信息） +1. 第 1 条 — 深入追问：基于当前话题，提出一个有洞察力的追问，帮助用户深入探索 +2. 第 2 条 — 实用行动：建议一个具体的、可操作的下一步（调用技能、执行工具、查看数据等） +3. 第 3 条 — 管家关怀： - 如果有未解决痛点 → 回访建议，如"上次你提到X，后来解决了吗？" - 如果有相关经验 → 引导复用，如"上次用X方法解决了类似问题，要再试试吗？" - 如果有匹配技能 → 推荐使用，如"你可以试试 [技能名] 来处理这个" - - 如果没有提供痛点/经验/技能信息 → 全部生成对话续问 -3. 每个不超过 30 个中文字符 -4. 不要重复对话中已讨论过的内容 -5. 使用与用户相同的语言 + - 如果没有提供痛点/经验/技能信息 → 给出一个启发性的思考角度 +4. 每个不超过 30 个中文字符 +5. 不要重复对话中已讨论过的内容 +6. 不要生成空泛的建议（如"继续分析"、"换个角度"） +7. 使用与用户相同的语言 只输出 JSON 数组，包含恰好 3 个字符串。不要输出任何其他内容。 -示例：["科室绩效分析可以按哪些维度拆解？", "上次的 researcher 技能能用在查房数据整理上吗？", "自动生成合规检查报告的模板有哪些？"]`, - user: (context: string) => `以下是对话中最近的消息：\n\n${context}\n\n请生成 3 个后续问题。`, +示例：["科室绩效分析可以按哪些维度拆解？", "用 researcher 技能帮你查一下相关文献？", "上次你提到排班冲突的问题，需要我再帮你想解决方案吗？"]`, + user: (context: string) => `以下是对话中最近的消息：\n\n${context}\n\n请生成 3 个后续建议（1 深入追问 + 1 实用行动 + 1 管家关怀）。`, }, }; diff --git a/desktop/src/store/chat/streamStore.ts b/desktop/src/store/chat/streamStore.ts index 224a5e6..945de77 100644 --- a/desktop/src/store/chat/streamStore.ts +++ b/desktop/src/store/chat/streamStore.ts @@ -40,6 +40,10 @@ import { fetchSuggestionContext, type SuggestionContext } from '../../lib/sugges const log = createLogger('StreamStore'); +// Module-level prefetch for suggestion context — started during streaming, +// consumed on stream completion. Saves ~0.5-1s vs fetching after stream ends. +let _activeSuggestionContextPrefetch: Promise | null = null; + // --------------------------------------------------------------------------- // Error formatting — convert raw LLM/API errors to user-friendly messages // --------------------------------------------------------------------------- @@ -400,44 +404,50 @@ function createCompleteHandler( } } - // Parallel: memory extraction + intelligence context fetch + // Decoupled: suggestion generation runs immediately with prefetched context, + // memory extraction + reflection run independently in background. const filtered = msgs .filter(m => m.role === 'user' || m.role === 'assistant') .map(m => ({ role: m.role, content: m.content })); const convId = useConversationStore.getState().currentConversationId; - const lastUserContent = typeof lastContent === 'string' ? lastContent : ''; - const suggestionContextPromise = fetchSuggestionContext(agentId, lastUserContent); + // Build conversation messages for suggestions + const latestMsgs = chat.getMessages() || []; + const conversationMessages = latestMsgs + .filter(m => m.role === 'user' || m.role === 'assistant') + .filter(m => !m.streaming) + .map(m => ({ role: m.role, content: m.content })); - // Fire-and-forget background tasks - Promise.all([ - getMemoryExtractor().extractFromConversation(filtered, agentId, convId ?? undefined) - .catch(err => log.warn('Memory extraction failed:', err)), - intelligenceClient.reflection.recordConversation() - .catch(err => log.warn('Recording conversation failed:', err)), - suggestionContextPromise, - ]).then(([, , context]) => { - // Conditional reflection (after context is ready) - intelligenceClient.reflection.shouldReflect().then(shouldReflect => { + // Consume prefetched context (started in sendMessage during streaming) + const prefetchPromise = _activeSuggestionContextPrefetch; + _activeSuggestionContextPrefetch = null; + + // Fire suggestion generation immediately — don't wait for memory extraction + const fireSuggestions = (ctx?: SuggestionContext) => { + generateLLMSuggestions(conversationMessages, set, ctx).catch(err => { + log.warn('Suggestion generation error:', err); + set({ suggestionsLoading: false }); + }); + }; + if (prefetchPromise) { + prefetchPromise.then(fireSuggestions).catch(() => fireSuggestions()); + } else { + fireSuggestions(); + } + + // Background tasks run independently — never block suggestions + getMemoryExtractor().extractFromConversation(filtered, agentId, convId ?? undefined) + .catch(err => log.warn('Memory extraction failed:', err)); + intelligenceClient.reflection.recordConversation() + .catch(err => log.warn('Recording conversation failed:', err)) + .then(() => intelligenceClient.reflection.shouldReflect()) + .then(shouldReflect => { if (shouldReflect) { intelligenceClient.reflection.reflect(agentId, []).catch(err => { log.warn('Reflection failed:', err); }); } - }); - - // Follow-up suggestions with enriched context - const latestMsgs = chat.getMessages() || []; - const conversationMessages = latestMsgs - .filter(m => m.role === 'user' || m.role === 'assistant') - .filter(m => !m.streaming) - .map(m => ({ role: m.role, content: m.content })); - - generateLLMSuggestions(conversationMessages, set, context).catch(err => { - log.warn('Suggestion generation error:', err); - set({ suggestionsLoading: false }); - }); - }); + }).catch(() => {}); }; } @@ -573,9 +583,9 @@ async function generateLLMSuggestions( set({ suggestionsLoading: true }); try { - const recentMessages = messages.slice(-6); + const recentMessages = messages.slice(-20); const conversationContext = recentMessages - .map(m => `${m.role === 'user' ? '用户' : '助手'}: ${m.content}`) + .map(m => `${m.role === 'user' ? '用户' : '助手'}: ${m.content.slice(0, 200)}`) .join('\n\n'); // Build dynamic user message with intelligence context @@ -799,6 +809,9 @@ export const useStreamStore = create()( }); set({ isStreaming: true, activeRunId: null }); + // Prefetch suggestion context during streaming — saves ~0.5-1s post-stream + _activeSuggestionContextPrefetch = fetchSuggestionContext(agentId, content); + // Delta buffer — batches updates at ~60fps const buffer = new DeltaBuffer(assistantId, _chat); @@ -1072,10 +1085,20 @@ export const useStreamStore = create()( .filter(m => !m.streaming) .map(m => ({ role: m.role, content: m.content })); - generateLLMSuggestions(conversationMessages, set).catch(err => { - log.warn('Suggestion generation error:', err); - set({ suggestionsLoading: false }); - }); + // Path B: use prefetched context for agent stream — fixes zero-personalization + const prefetchPromise = _activeSuggestionContextPrefetch; + _activeSuggestionContextPrefetch = null; + const fireSuggestions = (ctx?: SuggestionContext) => { + generateLLMSuggestions(conversationMessages, set, ctx).catch(err => { + log.warn('Suggestion generation error:', err); + set({ suggestionsLoading: false }); + }); + }; + if (prefetchPromise) { + prefetchPromise.then(fireSuggestions).catch(() => fireSuggestions()); + } else { + fireSuggestions(); + } } } } else if (delta.stream === 'hand') {