feat: 更新长期记忆工具提示，增强用户使用指导
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71a8033d15
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0331774466
@ -21,7 +21,7 @@ use std::time::Instant;
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const MAX_TOOL_RESULT_CHARS: usize = 16_000;
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/// Minimum characters to keep when truncating
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const TRUNCATION_SUFFIX_LEN: usize = 200;
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const MEMORY_AUTOSAVE_SYSTEM_PROMPT: &str = "你可以在处理任务过程中使用长期记忆工具。读取记忆时，优先使用 memory_search：当你需要用户长期偏好、稳定事实、历史决策、持续任务上下文时，先 search；已知 namespace/key 时可用 get；需要浏览最近记忆时可用 list。写入或修改记忆时，再使用 memory_manage。仅在遇到高价值且未来仍有用的信息时写入记忆：用户长期偏好、稳定事实、用户对你的纠正、持续任务/项目上下文、明确决策。不要保存一次性工具结果、临时列表、敏感凭证或不确定推测。写入时优先使用规范 namespace：preferences、profile、tasks、decisions，并优先调用 memory_manage(action='put')；同一 namespace/key 可直接覆盖更新。检索时尽量同时提供中文关键词、英文别名，以及可能的 snake_case memory_key 词，例如 email / 邮件 / email_folder_preference。";
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const MEMORY_TOOL_USAGE_SYSTEM_PROMPT: &str = "你可以在处理任务过程中使用长期记忆工具。读取记忆时，优先使用 memory_search：当你需要用户长期偏好、稳定事实、历史决策、持续任务上下文时，先 search；已知 namespace/key 时可用 get；需要浏览最近记忆时可用 list。写入或修改记忆时，再使用 memory_manage。仅在遇到高价值且未来仍有用的信息时写入记忆：用户长期偏好、稳定事实、用户对你的纠正、持续任务/项目上下文、明确决策。不要保存一次性工具结果、临时列表、敏感凭证或不确定推测。写入时优先使用规范 namespace：preferences、profile、tasks、decisions，并优先调用 memory_manage(action='put')；同一 namespace/key 可直接覆盖更新。检索时尽量同时提供中文关键词、英文别名，以及可能的 snake_case memory_key 词，例如 email / 邮件 / email_folder_preference。";
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const PENDING_USER_ACTION_MARKER: &str = "__PICOBOT_PENDING_USER_ACTION__";
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const DEFAULT_PENDING_ASSISTANT_MESSAGE: &str = "工具已经启动并进入等待用户操作的状态。请先完成外部操作，完成后直接告诉我继续。";
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@ -368,7 +368,7 @@ impl AgentLoop {
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if let Some(skill_prompt) = self.skills.system_index_prompt() {
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messages_for_llm.push(Message::system(skill_prompt));
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}
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messages_for_llm.push(Message::system(MEMORY_AUTOSAVE_SYSTEM_PROMPT));
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messages_for_llm.push(Message::system(MEMORY_TOOL_USAGE_SYSTEM_PROMPT));
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messages_for_llm.extend(messages.iter().map(chat_message_to_llm_message));
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// Build request
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@ -520,7 +520,6 @@ impl AgentLoop {
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if let Some(skill_prompt) = self.skills.system_index_prompt() {
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messages_for_llm.push(Message::system(skill_prompt));
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}
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messages_for_llm.push(Message::system(MEMORY_AUTOSAVE_SYSTEM_PROMPT));
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messages_for_llm.extend(messages.iter().map(chat_message_to_llm_message));
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let request = ChatCompletionRequest {
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@ -1,4 +1,4 @@
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use std::collections::{HashMap, HashSet};
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use std::collections::HashMap;
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use std::fs;
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use std::sync::Arc;
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use std::time::{Duration, Instant};
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@ -8,10 +8,9 @@ use uuid::Uuid;
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use crate::bus::{ChatMessage, MessageBus, OutboundMessage};
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use crate::config::LLMProviderConfig;
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use crate::agent::{AgentLoop, AgentError, ContextCompressor, EmittedMessageHandler};
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use crate::providers::{create_provider, ChatCompletionRequest, Message};
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use crate::protocol::WsOutbound;
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use crate::skills::SkillRuntime;
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use crate::storage::{MemoryRecord, SessionRecord, SessionStore, persistent_session_id};
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use crate::storage::{SessionRecord, SessionStore, persistent_session_id};
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use crate::tools::{
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BashTool, CalculatorTool, FileEditTool, FileReadTool, FileWriteTool,
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HttpRequestTool, MemoryManageTool, MemorySearchTool, SkillListTool, SkillManageTool, ToolContext, ToolRegistry,
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@ -19,10 +18,6 @@ use crate::tools::{
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};
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const DEFAULT_AGENT_PROMPT: &str = "# PicoBot 代理配置\n\n## 身份\n- 你是 PicoBot，一名务实、可靠的通用助理。\n- 你的目标是理解用户当下的真实需求，并给出清晰、可执行的帮助。\n\n## 工作方式\n- 优先理解意图，再给出回应或行动。\n- 保持简洁、准确、自然，不故作热情，也不空泛铺陈。\n- 能直接验证的内容尽量先验证，避免凭空猜测。\n- 当现有工具是完成任务的最直接方式时，优先使用工具。\n- 除非用户明确要求改变方向，否则保持用户原本目标不变。\n\n## 助理原则\n- 优先解决问题，而不是展示过程。\n- 输出要方便用户立即使用，结论尽量明确。\n- 对不确定的地方要直说，不把猜测包装成事实。\n- 复杂任务先收敛重点，简单任务直接给结果。\n- 避免不必要的重复、客套和冗长说明。\n\n## 回复规则\n- 除非用户另有要求，否则使用中文回复。\n- 默认短而清楚，按信息密度组织内容。\n- 如果任务涉及文件、命令、配置或下一步操作，优先给出最关键的那部分。\n- 如果存在限制、风险或前提条件，要直接说明。\n\n## 补充要求\n- 你是 PicoBot。\n- 回答应以帮助用户完成当前目标为中心。\n- 在信息不足时先补关键前提，在信息充分时直接执行。\n";
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const MEMORY_KEYWORD_SYSTEM_PROMPT: &str = "你负责为长期记忆检索生成关键词。根据给定的最近会话，仅输出 JSON 数组字符串。关键词必须同时覆盖中文和英文：优先为同一主题同时给出中文关键词和对应英文关键词。关键词必须是短词语，优先使用最容易命中记忆的核心检索词，不要输出完整句子、解释或长描述。必要时优先保留实体名、产品名、偏好名、snake_case key 风格短词。数组元素总数控制在 2 到 6 个简短关键词或短语。不要输出解释，不要输出 Markdown。如果最新的会话跟前面有明显主题变化，且新主题可能与旧主题的关键词不同，优先输出新主题的关键词；如果最新会话是旧主题的延续，优先输出旧主题的关键词；如果最新会话同时包含新旧主题，优先输出同时覆盖新旧主题的关键词。";
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const RELATED_MEMORY_SYSTEM_PROMPT_PREFIX: &str = "找到相关的记忆。你必须优先参考这些记忆，并在后续推理中把它们当作当前会话的补充上下文；若与用户本轮明确要求冲突，以用户本轮要求为准。";
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const MEMORY_KEYWORD_REASONING_EFFORT: &str = "none";
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const MEMORY_KEYWORD_MAX_CHARS: usize = 32;
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/// Session 按 channel 隔离，每个 channel 一个 Session
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/// History 按 chat_id 隔离，由 Session 统一管理
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@ -627,43 +622,7 @@ impl SessionManager {
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let user_message_id = user_message.id.clone();
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session_guard.append_persisted_message(chat_id, user_message)?;
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// 获取完整历史
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let mut history = session_guard.get_or_create_history(chat_id).clone();
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tracing::info!(
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channel = %channel_name,
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chat_id = %chat_id,
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sender_id = %sender_id,
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history_len = history.len(),
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"Starting synchronous related memory search"
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);
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if let Some(memory_prompt) = build_related_memory_prompt(
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session_guard.provider_config().clone(),
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self.store.clone(),
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channel_name.to_string(),
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sender_id.to_string(),
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chat_id.to_string(),
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history.clone(),
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)
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.await?
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{
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tracing::info!(
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channel = %channel_name,
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chat_id = %chat_id,
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sender_id = %sender_id,
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prompt_len = memory_prompt.len(),
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"Injecting related memory system prompt before agent processing"
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);
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let memory_message = ChatMessage::system(memory_prompt);
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session_guard.append_persisted_message(chat_id, memory_message.clone())?;
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history.push(memory_message);
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} else {
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tracing::info!(
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channel = %channel_name,
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chat_id = %chat_id,
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sender_id = %sender_id,
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"No related memory prompt generated before agent processing"
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);
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}
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let history = session_guard.get_or_create_history(chat_id).clone();
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// 压缩历史（如果需要）
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let history = session_guard.compressor
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@ -719,233 +678,6 @@ impl SessionManager {
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}
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}
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async fn build_related_memory_prompt(
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provider_config: LLMProviderConfig,
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store: Arc<SessionStore>,
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channel_name: String,
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sender_id: String,
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chat_id: String,
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history: Vec<ChatMessage>,
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) -> Result<Option<String>, AgentError> {
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let keywords = generate_memory_search_keywords(provider_config, &history).await?;
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tracing::info!(
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channel = %channel_name,
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chat_id = %chat_id,
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sender_id = %sender_id,
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keyword_count = keywords.len(),
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keywords = ?keywords,
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"Generated memory search keywords"
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);
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if keywords.is_empty() {
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return Ok(None);
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}
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let memories = search_related_memories(
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store,
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&channel_name,
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&sender_id,
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&chat_id,
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&keywords,
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)
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.await?;
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if memories.is_empty() {
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tracing::info!(
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channel = %channel_name,
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chat_id = %chat_id,
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sender_id = %sender_id,
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keyword_count = keywords.len(),
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"Related memory search returned no matches"
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);
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return Ok(None);
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}
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tracing::info!(
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channel = %channel_name,
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chat_id = %chat_id,
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sender_id = %sender_id,
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keyword_count = keywords.len(),
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memory_count = memories.len(),
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"Related memory search produced matches"
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);
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Ok(Some(format_related_memory_system_prompt(&keywords, &memories)))
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}
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async fn generate_memory_search_keywords(
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mut provider_config: LLMProviderConfig,
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history: &[ChatMessage],
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) -> Result<Vec<String>, AgentError> {
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if provider_config.provider_type == "openai" {
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provider_config.model_extra.insert(
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"reasoning_effort".to_string(),
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serde_json::Value::String(MEMORY_KEYWORD_REASONING_EFFORT.to_string()),
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);
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}
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let provider = create_provider(provider_config)
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.map_err(|err| AgentError::ProviderCreation(err.to_string()))?;
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let request = ChatCompletionRequest {
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messages: vec![
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Message::system(MEMORY_KEYWORD_SYSTEM_PROMPT),
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Message::user(build_memory_keyword_context(history)),
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],
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temperature: Some(0.0),
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max_tokens: Some(128),
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tools: None,
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};
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let response = provider
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.chat(request)
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.await
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.map_err(|err| AgentError::LlmError(err.to_string()))?;
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Ok(parse_memory_keywords(&response.content))
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}
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fn build_memory_keyword_context(history: &[ChatMessage]) -> String {
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let recent_messages: Vec<&ChatMessage> = history
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.iter()
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.rev()
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.filter(|message| message.role != "system")
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.take(8)
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.collect::<Vec<_>>()
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.into_iter()
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.rev()
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.collect();
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let mut lines = vec!["请基于以下最近会话生成长期记忆搜索关键词：".to_string()];
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for message in recent_messages {
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let content = message.content.trim();
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if content.is_empty() {
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continue;
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}
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let compact = if content.chars().count() > 240 {
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let prefix: String = content.chars().take(240).collect();
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format!("{}...", prefix)
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} else {
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content.to_string()
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};
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lines.push(format!("{}: {}", message.role, compact));
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}
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lines.join("\n")
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}
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fn parse_memory_keywords(raw: &str) -> Vec<String> {
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if let Ok(keywords) = serde_json::from_str::<Vec<String>>(raw) {
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return normalize_keywords(keywords);
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}
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normalize_keywords(
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raw.split(|ch| matches!(ch, '\n' | ',' | '，' | ';' | '；'))
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.map(str::trim)
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.filter(|part| !part.is_empty())
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.map(ToOwned::to_owned)
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.collect(),
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)
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}
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fn normalize_keywords(keywords: Vec<String>) -> Vec<String> {
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let mut seen = HashSet::new();
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let mut normalized = Vec::new();
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for keyword in keywords {
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let candidate = keyword
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.trim()
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.trim_matches('"')
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.trim_matches('[')
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.trim_matches(']')
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.trim()
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.to_string();
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let candidate = compact_memory_keyword(&candidate);
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if candidate.is_empty() {
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continue;
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}
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let key = candidate.to_lowercase();
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if seen.insert(key) {
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normalized.push(candidate);
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}
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if normalized.len() >= 6 {
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break;
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}
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}
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normalized
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}
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fn compact_memory_keyword(candidate: &str) -> String {
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let compact = candidate
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.split_whitespace()
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.next()
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.unwrap_or(candidate)
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.trim_matches(|ch: char| matches!(ch, '"' | '\'' | '[' | ']' | '。' | '，' | ',' | '；' | ';' | ':' | '：'))
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.trim();
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if compact.is_empty() {
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return String::new();
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}
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compact.chars().take(MEMORY_KEYWORD_MAX_CHARS).collect()
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}
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async fn search_related_memories(
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store: Arc<SessionStore>,
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channel_name: &str,
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sender_id: &str,
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chat_id: &str,
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keywords: &[String],
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) -> Result<Vec<MemoryRecord>, AgentError> {
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tracing::debug!(
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channel = %channel_name,
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chat_id = %chat_id,
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sender_id = %sender_id,
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keyword_count = keywords.len(),
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keywords = ?keywords,
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"Searching related memories with a single batched FTS query"
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);
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let scope_key = format!("{}:{}", channel_name, sender_id);
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let merged = store
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.search_memories_any("user", &scope_key, keywords, None, 10)
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.map_err(|err| AgentError::Other(format!("batched memory search error: {}", err)))?;
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tracing::info!(
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channel = %channel_name,
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chat_id = %chat_id,
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sender_id = %sender_id,
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keyword_count = keywords.len(),
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deduped_memory_count = merged.len(),
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"Finished related memory search aggregation"
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);
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Ok(merged)
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}
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fn format_related_memory_system_prompt(keywords: &[String], memories: &[MemoryRecord]) -> String {
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let mut lines = vec![
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RELATED_MEMORY_SYSTEM_PROMPT_PREFIX.to_string(),
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format!("检索关键词: {}", keywords.join(" / ")),
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"相关记忆: ".to_string(),
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];
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for (index, memory) in memories.iter().take(8).enumerate() {
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lines.push(format!(
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"{}. [{} / {}] {}",
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index + 1,
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memory.namespace,
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memory.memory_key,
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memory.content.replace('\n', " ").trim()
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));
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}
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lines.join("\n")
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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@ -1162,42 +894,4 @@ mod tests {
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assert_eq!(history[0].role, "system");
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}
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#[test]
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fn test_parse_memory_keywords_handles_json_and_dedup() {
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let keywords = parse_memory_keywords("[\"Rust\", \"偏好\", \"rust\", \"自动化\"]");
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assert_eq!(keywords, vec!["Rust", "偏好", "自动化"]);
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let fallback = parse_memory_keywords("Rust, 偏好\n自动化");
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assert_eq!(fallback, vec!["Rust", "偏好", "自动化"]);
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let compacted = parse_memory_keywords("[\"用户 身份 信息 长描述\", \"email_folder_preference details\"]");
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assert_eq!(compacted, vec!["用户", "email_folder_preference"]);
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}
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#[test]
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fn test_format_related_memory_system_prompt_includes_prefix_keywords_and_memory_lines() {
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let prompt = format_related_memory_system_prompt(
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&["Rust 偏好".to_string(), "审批项目".to_string()],
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&[MemoryRecord {
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id: "memory-1".to_string(),
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scope_kind: "user".to_string(),
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scope_key: "feishu:user-1".to_string(),
|
||||
namespace: "profile".to_string(),
|
||||
memory_key: "language".to_string(),
|
||||
content: "用户偏好 Rust 和自动化工具".to_string(),
|
||||
source_type: "message".to_string(),
|
||||
source_session_id: Some("feishu:chat-1".to_string()),
|
||||
source_message_id: Some("msg-1".to_string()),
|
||||
source_message_seq: Some(1),
|
||||
source_channel_name: Some("feishu".to_string()),
|
||||
source_chat_id: Some("chat-1".to_string()),
|
||||
created_at: 1,
|
||||
updated_at: 1,
|
||||
}],
|
||||
);
|
||||
|
||||
assert!(prompt.contains("找到相关的记忆"));
|
||||
assert!(prompt.contains("Rust 偏好 / 审批项目"));
|
||||
assert!(prompt.contains("[profile / language] 用户偏好 Rust 和自动化工具"));
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user