feat: 完善问问微信查询理解与检索链路

src/main/knowledge/knowledge-search-service.ts

@@ -81,6 +81,10 @@ function sourceMessageId(message: chat.FormattedMessage): string {
 
 function sourceKind(message: chat.FormattedMessage): KnowledgeMessageKind {
   if (message.voiceTranscript || message.type === '语音') return 'voice'
+  if (message.exportMediaType === 'image' || message.exportMediaType === 'video' || message.exportMediaType === 'sticker') {
+    return message.exportMediaType
+  }
+  if (message.exportMediaType === 'file') return 'file'
   if (message.contentData?.type === 'share' || message.contentData?.type === 'miniProgram') {
     return message.contentData.type === 'share' && message.contentData.typeVal === '6'
       ? 'file'
@@ -329,6 +333,7 @@ export class KnowledgeSearchService {
         senderIds: request.senderIds,
         startTime: request.startTime === undefined ? undefined : request.startTime * 1000,
         endTime: request.endTime === undefined ? undefined : request.endTime * 1000
+        ,conversationBoundary: request.conversationBoundary
       }
       const result = await this.searchKnowledge(searchRequest)
       // An existing derived database can answer while its next incremental pass is running.
@@ -481,12 +486,14 @@ export class KnowledgeSearchService {
       .filter(({ message, score }) => {
         const senderMatches = !senderIds.size || senderIds.has(message.senderId || message.from)
         const termMatches = !terms.length || score > 0
-        return senderMatches && termMatches
+        const boundaryMatches = !request.conversationBoundary || message.contentData?.type !== 'system'
+        return senderMatches && termMatches && boundaryMatches
       })
       .sort(
         (left, right) =>
           right.score - left.score ||
-          (right.message.createTime || 0) - (left.message.createTime || 0)
+          (request.conversationBoundary === 'first' ? -1 : 1) *
+            ((right.message.createTime || 0) - (left.message.createTime || 0))
       )
       .slice(0, Math.max(1, Math.min(request.limit || FALLBACK_LIMIT, FALLBACK_LIMIT)))
     const result: KnowledgeSearchIpcResult = {

src/main/knowledge/knowledge-store.ts

@@ -358,6 +358,36 @@ export class KnowledgeStore {
       ])
     ).filter(Boolean)
     const senderIds = new Set(query.senderIds || [])
+    if (query.conversationBoundary && conversationIds.length === 1) {
+      const direction = query.conversationBoundary === 'first' ? 'ASC' : 'DESC'
+      const row = this.database
+        .prepare(
+          `SELECT conversation_id, message_id, create_time, searchable_text, kind, sender_id, sender_name
+           FROM knowledge_messages
+           WHERE conversation_id = ? AND kind <> 'system'
+           ORDER BY create_time ${direction}, message_id ${direction}
+           LIMIT 1`
+        )
+        .get(conversationIds[0]) as DbRow | undefined
+      const count = this.database
+        .prepare('SELECT COUNT(*) AS total FROM knowledge_messages WHERE conversation_id = ?')
+        .get(conversationIds[0]) as DbRow | undefined
+      const indexState = this.database
+        .prepare('SELECT state, complete_snapshot FROM knowledge_index_state WHERE conversation_id = ?')
+        .get(conversationIds[0]) as DbRow | undefined
+      return {
+        evidence: row ? [this.metadataEvidence(row)] : [],
+        conversationRetrieval: {
+          conversationId: conversationIds[0],
+          totalMessages: Number(count?.total || 0),
+          chunkCount: 0,
+          candidateMessages: row ? 1 : 0,
+          systemMessagesDeprioritized: 0,
+          complete: String(indexState?.state || '') === 'ready' && Number(indexState?.complete_snapshot || 0) === 1
+        },
+        timings: { ...emptyKnowledgeSearchTimings(), messageLoadMs: Date.now() - startedAt, totalMs: Date.now() - startedAt }
+      }
+    }
     if (!terms.length) {
       const messageLoadStartedAt = Date.now()
       const metadata = this.searchByMetadata(query, conversationIds, senderIds)
@@ -832,10 +862,15 @@ export class KnowledgeStore {
         state TEXT NOT NULL,
         high_water_time INTEGER,
         indexed_message_count INTEGER NOT NULL DEFAULT 0,
+        complete_snapshot INTEGER NOT NULL DEFAULT 0,
         last_error TEXT,
         updated_at INTEGER NOT NULL
       ) STRICT;
     `)
+    const stateColumns = new Set(asRows(this.database.prepare('PRAGMA table_info(knowledge_index_state)').all()).map((row) => String(row.name)))
+    if (!stateColumns.has('complete_snapshot')) {
+      this.database.exec('ALTER TABLE knowledge_index_state ADD COLUMN complete_snapshot INTEGER NOT NULL DEFAULT 0')
+    }
     const messageColumns = new Set(
       asRows(this.database.prepare('PRAGMA table_info(knowledge_messages)').all()).map((row) =>
         String(row.name)
@@ -935,7 +970,9 @@ export class KnowledgeStore {
         chunker.version,
         'indexing',
         null,
-        normalized.length
+        normalized.length,
+        null,
+        conversation.completeSnapshot
       )
       await this.writeMessageLedger(
         conversation.conversationId,
@@ -982,7 +1019,9 @@ export class KnowledgeStore {
         chunker.version,
         'ready',
         highWater,
-        normalized.length
+        normalized.length,
+        null,
+        conversation.completeSnapshot
       )
       this.database.exec('COMMIT')
       return { chunkCount: chunks.length, updatedChunks: chunks.length }
@@ -1122,20 +1161,22 @@ export class KnowledgeStore {
     state: string,
     highWater: number | null,
     messageCount: number,
-    error: string | null = null
+    error: string | null = null,
+    completeSnapshot = false
   ): void {
     this.database
       .prepare(
         `INSERT INTO knowledge_index_state (
           conversation_id, account_id, chunker_version, state, high_water_time,
-          indexed_message_count, last_error, updated_at
+          indexed_message_count, complete_snapshot, last_error, updated_at
-        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+        ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
         ON CONFLICT(conversation_id) DO UPDATE SET
           account_id = excluded.account_id,
           chunker_version = excluded.chunker_version,
           state = excluded.state,
           high_water_time = excluded.high_water_time,
           indexed_message_count = excluded.indexed_message_count,
+          complete_snapshot = excluded.complete_snapshot,
           last_error = excluded.last_error,
           updated_at = excluded.updated_at`
       )
@@ -1146,6 +1187,7 @@ export class KnowledgeStore {
         state,
         highWater,
         messageCount,
+        completeSnapshot ? 1 : 0,
         error,
         Date.now()
       )

src/main/services/ai-search-pipeline-service.ts

@@ -4,6 +4,7 @@ import {
   buildLocalAiSearchPlan,
   inferAiSearchTimeRange,
   mergeAiSearchPlans,
+  parseAiQueryUnderstanding,
   parseAiSearchPlan,
   type AiSearchAgentRun,
   type AiSearchAgentTraceItem,
@@ -26,6 +27,7 @@ import { runControlledSearchAgent, type AgentAction, type AgentToolResult } from
 import { AIProviderService } from './ai-provider-service'
 import { KnowledgeSearchService } from '../knowledge/knowledge-search-service'
 import { resolveContact, type ContactResolutionScope } from './contact-resolution-service'
+import type { ContactResolutionResult } from '../../shared/contact-resolution'
 
 const DISPLAY_EVIDENCE_LIMIT = 8
 const AGENT_MESSAGE_LIMIT = 100
@@ -55,14 +57,32 @@ const contactScopeForIntent = (intent: AiSearchPlan['intent']): ContactResolutio
     : 'person'
 
 const isIdentityIntent = (intent: AiSearchPlan['intent']): boolean =>
+  intent === 'conversation_boundary' ||
   intent === 'conversation_recall' ||
   intent === 'conversation_topic_search' ||
   intent === 'conversation_name_search'
 
+const isIdentityPlan = (plan: AiSearchPlan): boolean =>
+  isIdentityIntent(plan.intent) || (plan.mode === 'semantic' && Boolean(plan.contactQuery || plan.targetQuery))
+
+export const isSuspiciousLocalPlan = (query: string, plan: AiSearchPlan): boolean => {
+  const hasRelationalShape = /我\s*(?:和|跟|与)|(?:和|跟|与)\s*我|第一次|最早|最后一次|最近一次/.test(query)
+  const hasBoundaryShape = /第一次|最早|最后一次|最近一次|上一次|什么时候开始/.test(query)
+  const hasInterpretiveShape = /熟起来|刚认识|答应|是不是|提过|后来|那个|事情/.test(query)
+  return (
+    (plan.intent === 'global_topic_search' && hasRelationalShape) ||
+    (hasInterpretiveShape && plan.intent !== 'conversation_boundary') ||
+    (hasBoundaryShape && plan.intent !== 'conversation_boundary') ||
+    (hasRelationalShape && !plan.contactQuery)
+  )
+}
+
 const retrievalModeForIntent = (
   intent: AiSearchPlan['intent']
 ): AiSearchRetrievalContract['retrievalMode'] =>
-  intent === 'conversation_recall'
+  intent === 'conversation_boundary'
+    ? 'conversation_boundary'
+    : intent === 'conversation_recall'
     ? 'conversation_metadata'
     : intent === 'conversation_topic_search'
       ? 'conversation_topic_fts'
@@ -97,6 +117,11 @@ const conversationIdsForContacts = (contacts: Contact[]): string[] =>
 const messageTime = (timestamp: number): string =>
   new Date(timestamp).toLocaleString('zh-CN', { hour12: false })
 
+const queryDate = (timestamp: number): string => {
+  const date = new Date(timestamp * 1000)
+  return `${date.getFullYear()}年${date.getMonth() + 1}月${date.getDate()}日`
+}
+
 const estimateTokens = (value: string): number => Math.ceil(value.length / 2)
 
 const emptyAggregation = (): AiSearchAggregation => ({
@@ -259,6 +284,7 @@ export class AiSearchPipelineService {
     const localPlan = buildLocalAiSearchPlan(request.text)
     let plan: AiSearchPlan = {
       ...localPlan,
+      mode: 'structured',
       scopeLabel: aiSearchScopeLabel(
         localPlan.intent === 'global_group_topic_search' && request.scope === 'global'
           ? 'groups'
@@ -288,14 +314,106 @@ export class AiSearchPipelineService {
       const contactResolutionStartedAt = Date.now()
       const contacts = chat.isReady() ? await chat.listContactsAsync() : []
       signal.throwIfAborted()
+      const shouldUseQueryUnderstanding =
+        localPlan.intent === 'general' || isSuspiciousLocalPlan(request.text, plan)
+      let queryUnderstandingSource: 'local' | 'ai' = 'local'
+      let queryUnderstandingError: string | undefined
+      if (shouldUseQueryUnderstanding && aiSearchAvailable) {
+        let parserResult: Awaited<ReturnType<AiSearchPipelineService['chatForSearchRequest']>>
+        let parserException: string | undefined
+        try {
+          parserResult = await this.chatForSearchRequest(
+            request.requestId,
+            aiConfig.providerId,
+            aiConfig.model,
+            [
+              {
+                role: 'system',
+                content:
+                  '你是 TraceMemo Query Compiler。只输出严格 JSON，不回答问题、不搜索聊天、不编造联系人。mode 只能是 structured、semantic、clarification。structured 复用已有 intent；conversation_boundary 允许 boundary=first|last 和 projection=time|content|time_and_content。semantic 用 semanticQuery（1-200 字）、queryVariants（最多 4 个）、可选 targetQuery（联系人显示名）和 answerMode=extract|synthesis；clarification 仅用于确实缺少必要上下文且 requiresClarification=true。只允许输出 schema 中字段：mode、intent、contactQuery、topicQuery、boundary、projection、targetQuery、semanticQuery、queryVariants、answerMode、confidence、requiresClarification、clarificationReason。禁止 conversationId、wxid、messageId、SQL、路径、Evidence、时间戳、Tool、Provider。',
+              },
+              {
+                role: 'user',
+                content: `用户问题：${request.text}\n当前界面范围：${request.scope}`
+              }
+            ],
+            signal
+          )
+        } catch (error) {
+          parserException = error instanceof Error ? error.message : '查询语义解析器执行失败'
+          parserResult = {
+            success: false,
+            error: parserException
+          }
+        }
+        const understanding = parserResult.success && parserResult.data
+          ? parseAiQueryUnderstanding(parserResult.data)
+          : null
+        if (understanding && understanding.confidence >= 0.6 && !understanding.requiresClarification) {
+          queryUnderstandingSource = 'ai'
+          plan = {
+            ...plan,
+            intent: understanding.intent,
+            mode: understanding.mode || 'structured',
+            contactQuery: understanding.contactQuery || understanding.targetQuery,
+            targetQuery: understanding.targetQuery,
+            semanticQuery: understanding.semanticQuery,
+            queryVariants: understanding.queryVariants,
+            answerMode: understanding.answerMode,
+            topicQuery: understanding.topicQuery,
+            boundary: understanding.boundary,
+            projection: understanding.projection,
+            keywords: understanding.topicQuery ? [understanding.topicQuery] : [],
+            variants: understanding.topicQuery ? [understanding.topicQuery] : [],
+            source: 'hybrid'
+          }
+        } else {
+          queryUnderstandingError =
+            understanding?.clarificationReason || parserException || parserResult.error || '无法可靠理解查询意图'
+        }
+      } else if (shouldUseQueryUnderstanding) {
+        queryUnderstandingError = aiConfig.configured ? '查询语义解析器暂时不可用' : '尚未配置可用 AI 模型'
+      }
       const selectedContact =
         request.scope === 'conversation' && request.conversationId
           ? contacts.find((contact) => contact.md5 === request.conversationId)
           : undefined
       const sourceContacts = this.scopeContacts(contacts, request, selectedContact, plan.intent)
-      if (!sourceContacts.length) throw new Error('当前搜索范围没有可用会话')
+      if (!sourceContacts.length && !queryUnderstandingError) throw new Error('当前搜索范围没有可用会话')
-      const contactResolution = plan.contactQuery
+      if (queryUnderstandingError) {
-        ? resolveContact(plan.contactQuery, sourceContacts, contactScopeForIntent(plan.intent))
+        const retrieval: AiSearchRetrievalContract = {
+          intent: plan.intent,
+          timeRange: plan.timeRange,
+          retrievalMode: 'unresolved_identity',
+          candidateCount: 0,
+          uniqueCandidateCount: 0,
+          sourceCoverage: 'unknown',
+          isComplete: false,
+          fallbackUsed: false,
+          suspicious: true
+        }
+        const error = `我没有完全理解你想怎么查：${queryUnderstandingError}`
+        emit({ stage: 'query_understanding', status: 'error', message: error, plan, error })
+        return {
+          requestId: request.requestId,
+          status: 'understanding_failed',
+          plan,
+          knowledge: { source: 'knowledge', state: 'unavailable', indexedMessageCount: 0, indexedChunkCount: 0, totalMessages: 0 },
+          candidateEvidenceCount: 0,
+          retrieval,
+          evidence: [],
+          evidenceCollection: [],
+          contextEvidenceCount: 0,
+          aggregation: emptyAggregation(),
+          agent: { mode: 'fallback', toolCalls: 0, trace: [], fallbackReason: error },
+          timings: snapshotTimings(),
+          error,
+          errorStage: 'query_understanding',
+          elapsedMs: Date.now() - startedAt
+        }
+      }
+      const contactResolution = (plan.contactQuery || plan.targetQuery)
+        ? resolveContact(plan.contactQuery || plan.targetQuery || '', sourceContacts, contactScopeForIntent(plan.intent))
         : undefined
       const resolvedContact =
         selectedContact ||
@@ -313,7 +431,7 @@ export class AiSearchPipelineService {
         contactNames: resolvedContact ? [contactLabel(resolvedContact)] : []
       }
       const conversationIds =
-        isIdentityIntent(plan.intent) && resolvedContact
+        isIdentityPlan(plan) && resolvedContact
           ? [resolvedContact.md5]
           : request.scope === 'global' && plan.intent !== 'global_group_topic_search'
             ? undefined
@@ -323,7 +441,7 @@ export class AiSearchPipelineService {
       let agent: AiSearchAgentRun = { mode: 'fallback', toolCalls: 0, trace: [] }
       let candidateEvidence: AiSearchPipelineEvidence[]
       let searchResult: KnowledgeSearchIpcResult
-      const agentOutcome = aiSearchAvailable
+      const agentOutcome = aiSearchAvailable && plan.intent !== 'conversation_boundary' && plan.mode !== 'semantic'
         ? await this.runAgentSearch(
             request,
             plan,
@@ -387,8 +505,8 @@ export class AiSearchPipelineService {
         timings.rankingMs += agentOutcome.searchTimings.rankingMs
       } else {
         const deterministicIdentityRetrieval =
-          Boolean(resolvedContact) && (isIdentityIntent(plan.intent) || Boolean(selectedContact))
+          Boolean(resolvedContact) && (isIdentityPlan(plan) || Boolean(selectedContact))
-        const unresolvedIdentity = isIdentityIntent(plan.intent) && !resolvedContact
+        const unresolvedIdentity = isIdentityPlan(plan) && !resolvedContact
         const fallbackReason = confirmedConversationNeedsFallback
           ? selectedContact
             ? '已选择会话的 Agent 未产生可读取消息，已按该会话执行确定性检索'
@@ -423,7 +541,7 @@ export class AiSearchPipelineService {
           agentTrace: agent.trace[0],
           timings: snapshotTimings()
         })
-        if (aiSearchAvailable && !deterministicIdentityRetrieval && !unresolvedIdentity) {
+        if (aiSearchAvailable && plan.mode !== 'semantic' && !deterministicIdentityRetrieval && !unresolvedIdentity) {
           const planningStartedAt = Date.now()
           const planning = await this.chatForSearchRequest(
             request.requestId,
@@ -476,22 +594,46 @@ export class AiSearchPipelineService {
         } else {
           const knowledgeSearchStartedAt = Date.now()
           const deterministicTerms =
-            plan.intent === 'conversation_recall' || plan.intent === 'conversation_name_search'
+            plan.mode === 'semantic'
-              ? []
+              ? Array.from(new Set([plan.semanticQuery, ...(plan.queryVariants || [])].filter((term): term is string => Boolean(term)))).slice(0, 5)
-              : plan.intent === 'conversation_topic_search'
+              : plan.intent === 'conversation_recall' || plan.intent === 'conversation_name_search'
-                ? plan.topicQuery
+                ? []
-                  ? [plan.topicQuery]
+                : plan.intent === 'conversation_topic_search'
-                  : []
+                  ? plan.topicQuery
-                : Array.from(new Set([...plan.keywords, ...plan.variants]))
+                    ? [plan.topicQuery]
-          searchResult = await this.knowledge.search({
+                    : []
-            text: request.text,
+                  : Array.from(new Set([...plan.keywords, ...plan.variants]))
-            terms: deterministicTerms,
+          const semanticResults: KnowledgeSearchIpcResult[] = []
-            retrievalSessionId: request.requestId,
+          const termsToSearch: string[][] =
-            conversationIds,
+            plan.mode === 'semantic' ? deterministicTerms.map((term) => [term]) : [deterministicTerms]
-            startTime: plan.timeRange.startTime,
+          for (const terms of termsToSearch) {
-            endTime: plan.timeRange.endTime,
+            semanticResults.push(await this.knowledge.search({
-            limit: 240
+              text: request.text,
-          })
+              terms,
+              retrievalSessionId: request.requestId,
+              conversationIds,
+              startTime: plan.timeRange.startTime,
+              endTime: plan.timeRange.endTime,
+              conversationBoundary: plan.boundary,
+              limit: 240
+            }))
+          }
+          const firstResult = semanticResults[0]
+          const mergedEvidence = Array.from(
+            new Map(semanticResults.flatMap((result) => result.evidence).map((item) => [`${item.conversationId}\u0000${item.messageId}`, item])).values()
+          )
+          searchResult = {
+            ...(firstResult || {
+              source: 'knowledge', state: 'ready', indexedMessageCount: 0, indexedChunkCount: 0,
+              totalMessages: 0, evidence: [], timings: emptyKnowledgeSearchTimings()
+            }),
+            evidence: mergedEvidence,
+            indexedMessageCount: Math.max(...semanticResults.map((result) => result.indexedMessageCount), 0),
+            indexedChunkCount: Math.max(...semanticResults.map((result) => result.indexedChunkCount), 0),
+            totalMessages: Math.max(...semanticResults.map((result) => result.totalMessages), 0),
+            conversationRetrieval: semanticResults.find((result) => result.conversationRetrieval)?.conversationRetrieval,
+            voiceCoverage: semanticResults.find((result) => result.voiceCoverage)?.voiceCoverage
+          }
           signal.throwIfAborted()
           timings.knowledgeSearchMs += Date.now() - knowledgeSearchStartedAt
           candidateEvidence = this.toPipelineEvidence(searchResult, contacts)
@@ -516,7 +658,7 @@ export class AiSearchPipelineService {
       emit({
         stage: 'query_understanding',
         status: 'completed',
-        message: '已理解搜索条件',
+        message: queryUnderstandingSource === 'ai' ? '已通过查询语义解析器理解搜索条件' : '已理解搜索条件',
         plan,
         timings: snapshotTimings()
       })
@@ -580,7 +722,8 @@ export class AiSearchPipelineService {
         resolvedContact,
         searchResult,
         candidateEvidence,
-        agent
+        agent,
+        contactResolution
       )
       if (retrieval.suspicious && resolvedContact) {
         // An identity route that somehow yielded 0/1 records is never allowed
@@ -593,6 +736,7 @@ export class AiSearchPipelineService {
           conversationIds: [resolvedContact.md5],
           startTime: plan.timeRange.startTime,
           endTime: plan.timeRange.endTime,
+          conversationBoundary: plan.boundary,
           limit: 240
         })
         signal.throwIfAborted()
@@ -603,7 +747,8 @@ export class AiSearchPipelineService {
           resolvedContact,
           searchResult,
           candidateEvidence,
-          agent
+          agent,
+          contactResolution
         )
       }
 
@@ -717,7 +862,30 @@ export class AiSearchPipelineService {
         timings: snapshotTimings(),
         elapsedMs: Date.now() - startedAt
       }
+      if (plan.intent === 'conversation_boundary' && retrieval.identityResolution !== 'resolved') {
+        const ambiguous = retrieval.identityResolution === 'ambiguous'
+        const error = ambiguous
+          ? `联系人“${plan.contactQuery || ''}”存在多个匹配，请先确认具体联系人。`
+          : `没有确认联系人“${plan.contactQuery || ''}”，无法查询会话边界。`
+        return {
+          ...baseResult,
+          status: ambiguous ? 'ambiguous_contact' : 'contact_not_found',
+          error,
+          timings: snapshotTimings(),
+          elapsedMs: Date.now() - startedAt
+        }
+      }
       if (!evidence.length) {
+        if (plan.intent === 'conversation_boundary') {
+          const error = `已确认联系人“${plan.contactQuery || ''}”，但当前知识库没有可读取的聊天消息。`
+          return {
+            ...baseResult,
+            status: 'no_messages',
+            error,
+            timings: snapshotTimings(),
+            elapsedMs: Date.now() - startedAt
+          }
+        }
         emit({
           stage: 'completed',
           status: 'completed',
@@ -734,6 +902,51 @@ export class AiSearchPipelineService {
         }
       }
 
+      if (plan.intent === 'conversation_boundary') {
+        const item = evidence[0]
+        const boundaryLabel = plan.boundary === 'first' ? '最早' : '最后'
+        const coverageNote = retrieval.isComplete
+          ? '当前知识库已完成该会话的可读取记录覆盖。'
+          : '当前知识库覆盖不完整，不能据此确认历史上的第一次或最后一次交流。'
+        const projection = plan.projection || 'time'
+        const content = item.sourceKind === 'text'
+          ? item.text || '（文本消息为空）'
+          : item.sourceKind === 'voice'
+            ? item.text ? `语音转写：${item.text}` : '语音消息（没有可用转写）'
+            : item.sourceKind === 'file'
+              ? '文件消息（没有可展示文本）'
+              : item.sourceKind === 'image'
+                ? '图片消息（没有可展示文本）'
+                : item.sourceKind === 'video'
+                  ? '视频消息（没有可展示文本）'
+                  : item.sourceKind === 'sticker'
+                    ? '表情消息（没有可展示文本）'
+              : item.sourceKind === 'link'
+                ? '链接消息（没有可展示文本）'
+                : '非文本消息（没有可展示文本）'
+        const timeLine = `在 TraceMemo 当前可读取的聊天记录中，你和${plan.contactQuery || item.conversationName} ${boundaryLabel}的一条聊天记录出现在 ${messageTime(item.timestamp)}。`
+        const contentLine = `${item.sender}：${content} [E1]`
+        const answer = projection === 'content'
+          ? `在 TraceMemo 当前可读取的聊天记录中，你和${plan.contactQuery || item.conversationName} ${boundaryLabel}的一条聊天记录内容是：\n${contentLine}\n${coverageNote}`
+          : `${timeLine}\n${contentLine}\n${coverageNote}`
+        emit({
+          stage: 'completed',
+          status: 'completed',
+          message: '已确定会话边界并生成可追溯答案',
+          plan,
+          stats: { matchedMessages: 1, evidenceCount: 1 },
+          timings: snapshotTimings()
+        })
+        return {
+          ...baseResult,
+          status: 'completed',
+          answer,
+          citationValidation: { status: 'valid', invalidCitationIds: [] },
+          timings: snapshotTimings(),
+          elapsedMs: Date.now() - startedAt
+        }
+      }
+
       if (retrieval.suspicious) {
         const error = '已找到目标会话，但当前检索未完整覆盖聊天记录，未生成总结。'
         emit({
@@ -1681,6 +1894,7 @@ export class AiSearchPipelineService {
           : ''
     return `检索范围：${plan.scopeLabel}，时间：${plan.rangeLabel}
 用户问题：${query}
+当前查询实际时间范围：${plan.timeRange.startTime === undefined ? '未限定开始日期' : queryDate(plan.timeRange.startTime)} 至 ${plan.timeRange.endTime === undefined ? '当前时刻' : queryDate(plan.timeRange.endTime)}。回答只能基于这个程序确定的时间范围，不得根据“上个月”“去年”等原句自行推算其他年份。
 检索意图：${aiSearchIntentLabel(plan.intent)}
 检索关键词：${plan.keywords.join('、') || '未提取到主题关键词'}
 检索范围消息总数：${totalMessages}
@@ -1701,9 +1915,10 @@ ${context}`
     resolvedContact: Contact | undefined,
     result: KnowledgeSearchIpcResult,
     candidates: AiSearchPipelineEvidence[],
-    agent: AiSearchAgentRun
+    agent: AiSearchAgentRun,
+    contactResolution?: ContactResolutionResult
   ): AiSearchRetrievalContract {
-    const identity = isIdentityIntent(plan.intent)
+    const identity = isIdentityPlan(plan)
     const conversationRetrieval = result.conversationRetrieval
     const sourceMessageCount =
       conversationRetrieval?.totalMessages ??
@@ -1743,6 +1958,14 @@ ${context}`
       fallbackUsed: agent.mode === 'fallback' || result.source === 'fallback',
       fallbackReason: agent.fallbackReason || result.fallbackReason,
       voiceCoverage: result.voiceCoverage,
+      identityResolution: identity
+        ? resolvedContact
+          ? 'resolved'
+          : contactResolution?.ambiguous
+            ? 'ambiguous'
+            : 'not_found'
+        : 'not_required',
+      boundary: plan.boundary,
       suspicious:
         plan.intent === 'conversation_recall' &&
         Boolean(resolvedContact) &&

src/renderer/src/components/search/AISearchWorkspace.tsx

@@ -236,6 +236,9 @@ export function AISearchWorkspace({
       range,
       timeRangeOverride,
       activeContactMd5: activeContact?.md5,
+      knowledgeGeneration: knowledgeStatus
+        ? `${knowledgeStatus.state}:${knowledgeStatus.indexedMessageCount}:${knowledgeStatus.indexedChunkCount}:${knowledgeStatus.processedMessages}`
+        : undefined,
       retry
     })
     if (!normalizedQuery) {
@@ -672,8 +675,12 @@ export function AISearchWorkspace({
           <span className="ai-search-kicker">✓ 已完成</span>
           <h2>{resultQuery || query}</h2>
           <p>
-            知识库已收录 {messageCount.toLocaleString()} 条消息 → 找到{' '}
+            知识库已收录 {messageCount.toLocaleString()} 条消息 →{' '}
-            {searchTrace?.retrievedEvidence || 0} 条相关消息 → {evidence.length} 条 Evidence →
+            {cachedAt
+              ? `缓存中保留 ${evidenceCollection.length} 条 Evidence`
+              : searchTrace?.retrievedEvidence !== undefined
+                ? `读取 ${searchTrace.retrievedEvidence} 条范围消息`
+                : `读取 ${evidence.length} 条消息`}{' '}→ {evidence.length} 条 Evidence →
             已生成回答{cachedAt ? ' · 已使用缓存' : ''}
           </p>
           {searchTrace &&

src/renderer/src/components/search/hooks/useSearchHistory.ts

@@ -1,5 +1,5 @@
 import { useEffect, useRef, useState, type Dispatch, type SetStateAction } from 'react'
-import { aiSearchRangeStart } from '../../../../../shared/ai-search'
+import { aiSearchRangeStart, inferAiSearchTimeRange } from '../../../../../shared/ai-search'
 import type { AiSearchTimeRange } from '../../../../../shared/ai-search'
 import {
   RANGE_LABELS,
@@ -9,7 +9,6 @@ import {
   buildSearchCacheKey,
   parseSearchCacheKey,
   readSearchCache,
-  readSearchCacheByQuery,
   writeSearchCache
 } from '../searchUtils'
 import { createSearchCacheRecord, mapCacheRecordToResult } from '../searchMappers'
@@ -178,13 +177,15 @@ export function useSearchHistory({
     setQuery(historyQuery)
     setSelectedEvidence(0)
     setHistoryOpen(false)
+    const resolvedTimeRange = inferAiSearchTimeRange(historyQuery, range, new Date())
     const cacheKey = buildSearchCacheKey(
       scope,
       scope === 'conversation' ? conversationContactMd5 : '',
       range,
-      historyQuery
+      historyQuery,
+      resolvedTimeRange
     )
-    const cached = readSearchCache(cacheKey) || readSearchCacheByQuery(historyQuery)?.record || null
+    const cached = readSearchCache(cacheKey)
     if (!cached) {
       setAnswer('')
       setEvidence([])

src/renderer/src/components/search/searchMappers.ts

@@ -58,7 +58,10 @@ export const mapSearchResultToTrace = (
   finalEvidenceCount: number
 ): SearchTrace => ({
   knowledgeMessages: result.knowledge.indexedMessageCount,
-  retrievedEvidence: result.candidateEvidenceCount,
+  retrievedEvidence:
+    result.retrieval.intent === 'conversation_recall'
+      ? result.retrieval.sourceMessageCount ?? result.candidateEvidenceCount
+      : result.candidateEvidenceCount,
   finalEvidence: finalEvidenceCount,
   timings: result.timings,
   contextEvidence: result.contextEvidenceCount,

src/renderer/src/components/search/searchState.ts

@@ -51,6 +51,30 @@ export const resolveSearchResultViewTransition = (
   result: AiSearchPipelineResult,
   range: SearchRange
 ): SearchResultViewTransition => {
+  if (result.status === 'understanding_failed') {
+    return {
+      stage: 'insufficient',
+      analysisError: result.error || '我没有完全理解你想怎么查，可以换一种说法。'
+    }
+  }
+  if (result.status === 'contact_not_found') {
+    return {
+      stage: 'insufficient',
+      analysisError: result.error || '我理解你在问这个联系人，但没有在当前通讯录中确认到对应联系人。'
+    }
+  }
+  if (result.status === 'ambiguous_contact') {
+    return {
+      stage: 'insufficient',
+      analysisError: result.error || '找到多个可能的联系人，暂时无法确定你指的是哪一个。'
+    }
+  }
+  if (result.status === 'no_messages') {
+    return {
+      stage: 'insufficient',
+      analysisError: result.error || '已经确认联系人，但当前可读取记录里没有对应聊天消息。'
+    }
+  }
   if (result.status === 'no_evidence') {
     return {
       stage: 'insufficient',

src/renderer/src/components/search/searchUtils.ts

@@ -1,5 +1,5 @@
 import type { Contact, Message } from '../../../../shared/types'
-import type { AiSearchTimeRange } from '../../../../shared/ai-search'
+import { inferAiSearchTimeRange, type AiSearchTimeRange } from '../../../../shared/ai-search'
 import type {
   AISearchCacheRecord,
   EvidenceItem,
@@ -315,8 +315,23 @@ export const buildSearchCacheKey = (
   scope: SearchScope,
   contactMd5: string,
   range: SearchRange,
-  query: string
+  query: string,
-): string => JSON.stringify([scope, contactMd5, range, query.trim().toLowerCase()])
+  resolvedTimeRange?: Pick<AiSearchTimeRange, 'startTime' | 'endTime'>,
+  knowledgeGeneration?: string
+): string => {
+  const base = [scope, contactMd5, range, query.trim().toLowerCase()]
+  const hasResolvedTime = Boolean(
+    resolvedTimeRange &&
+      (resolvedTimeRange.startTime !== undefined || resolvedTimeRange.endTime !== undefined)
+  )
+  if (!hasResolvedTime && !knowledgeGeneration) return JSON.stringify(base)
+  return JSON.stringify([
+    ...base,
+    hasResolvedTime ? resolvedTimeRange?.startTime ?? null : null,
+    hasResolvedTime ? resolvedTimeRange?.endTime ?? null : null,
+    knowledgeGeneration || null
+  ])
+}
 
 export type CreateSearchRequestContextInput = {
   query: string
@@ -324,6 +339,8 @@ export type CreateSearchRequestContextInput = {
   range: SearchRange
   timeRangeOverride?: AiSearchTimeRange
   activeContactMd5?: string
+  now?: Date
+  knowledgeGeneration?: string
   retry?: {
     range: SearchRange
     timeRangeOverride?: AiSearchTimeRange
@@ -335,6 +352,7 @@ export type SearchRequestContext = {
   effectiveRange: SearchRange
   effectiveTimeRangeOverride?: AiSearchTimeRange
   conversationId?: string
+  resolvedTimeRange: AiSearchTimeRange
   cacheKey: string
 }
 
@@ -344,25 +362,41 @@ export const createSearchRequestContext = ({
   range,
   timeRangeOverride,
   activeContactMd5,
+  now,
+  knowledgeGeneration,
   retry
 }: CreateSearchRequestContextInput): SearchRequestContext => {
   const normalizedQuery = query.trim()
   const effectiveRange = retry?.range || range
   const effectiveTimeRangeOverride = retry?.timeRangeOverride || timeRangeOverride
   const conversationId = scope === 'conversation' ? activeContactMd5 : undefined
+  const resolvedTimeRange = inferAiSearchTimeRange(
+    normalizedQuery,
+    effectiveRange,
+    now || new Date(),
+    effectiveTimeRangeOverride
+  )
 
   return {
     normalizedQuery,
     effectiveRange,
     effectiveTimeRangeOverride,
     conversationId,
-    cacheKey: buildSearchCacheKey(scope, conversationId || '', effectiveRange, normalizedQuery)
+    resolvedTimeRange,
+    cacheKey: buildSearchCacheKey(
+      scope,
+      conversationId || '',
+      effectiveRange,
+      normalizedQuery,
+      resolvedTimeRange,
+      knowledgeGeneration
+    )
   }
 }
 
 export const parseSearchCacheKey = (
   key: string
-): { scope: SearchScope; contactMd5: string; range: SearchRange; query: string } | null => {
+): { scope: SearchScope; contactMd5: string; range: SearchRange; query: string; startTime?: number; endTime?: number } | null => {
   try {
     const parts = JSON.parse(key) as unknown
     if (
@@ -377,7 +411,9 @@ export const parseSearchCacheKey = (
       scope: parts[0] as SearchScope,
       contactMd5: typeof parts[1] === 'string' ? parts[1] : '',
       range: parts[2] as SearchRange,
-      query: parts[3]
+      query: parts[3],
+      startTime: typeof parts[4] === 'number' ? parts[4] : undefined,
+      endTime: typeof parts[5] === 'number' ? parts[5] : undefined
     }
   } catch {
     return null

src/shared/ai-search.ts

@@ -6,12 +6,16 @@ import type {
 
 export type AiSearchScope = 'global' | 'groups' | 'contacts' | 'conversation'
 export type AiSearchRange = 'today' | '7d' | '30d' | 'all'
+export type AiSearchProjection = 'time' | 'content' | 'time_and_content'
+export type AiSearchQueryMode = 'structured' | 'semantic' | 'clarification'
+export type AiSearchAnswerMode = 'extract' | 'synthesis'
 /**
  * Retrieval semantics, not presentation labels. Each intent has a constrained
  * execution path in the main process; a model must not be able to quietly turn
  * an identity lookup into a generic message-keyword search.
  */
 export type AiSearchIntent =
+  | 'conversation_boundary'
   | 'conversation_recall'
   | 'conversation_topic_search'
   | 'global_sender_topic_search'
@@ -44,6 +48,7 @@ export type AiSearchProgressStage =
 export type AiSearchProgressStatus = 'running' | 'completed' | 'error'
 
 export interface AiSearchPlan {
+  mode?: AiSearchQueryMode
   intent: AiSearchIntent
   keywords: string[]
   variants: string[]
@@ -56,6 +61,28 @@ export interface AiSearchPlan {
   contactQuery?: string
   /** The message-content query, never a contact display name. */
   topicQuery?: string
+  boundary?: 'first' | 'last'
+  projection?: AiSearchProjection
+  targetQuery?: string
+  semanticQuery?: string
+  queryVariants?: string[]
+  answerMode?: AiSearchAnswerMode
+}
+
+export interface AiQueryUnderstanding {
+  mode?: AiSearchQueryMode
+  intent: AiSearchIntent
+  contactQuery?: string
+  topicQuery?: string
+  boundary?: 'first' | 'last'
+  projection?: AiSearchProjection
+  targetQuery?: string
+  semanticQuery?: string
+  queryVariants?: string[]
+  answerMode?: AiSearchAnswerMode
+  confidence: number
+  requiresClarification: boolean
+  clarificationReason?: string
 }
 
 export interface AiSearchPipelineRequest {
@@ -222,6 +249,7 @@ export interface AiSearchRetrievalContract {
   conversationId?: string
   timeRange: AiSearchTimeRange
   retrievalMode:
+    | 'conversation_boundary'
     | 'conversation_metadata'
     | 'conversation_topic_fts'
     | 'global_fts'
@@ -236,12 +264,18 @@ export interface AiSearchRetrievalContract {
   fallbackReason?: string
   suspicious: boolean
   voiceCoverage?: KnowledgeVoiceCoverage
+  identityResolution?: 'not_required' | 'resolved' | 'not_found' | 'ambiguous'
+  boundary?: 'first' | 'last'
 }
 
 export interface AiSearchPipelineResult {
   requestId: string
   status:
     | 'completed'
+    | 'understanding_failed'
+    | 'contact_not_found'
+    | 'ambiguous_contact'
+    | 'no_messages'
     | 'no_evidence'
     | 'retrieval_incomplete'
     | 'ai_failed'
@@ -434,16 +468,39 @@ export const inferAiSearchTimeRange = (
   }
   if (/这个月|本月/.test(query)) return fromQuery(currentMonthStart(now), '本月', '用户说“这个月”')
   if (/上个月/.test(query)) {
-    const start = Math.floor(new Date(now.getFullYear(), now.getMonth() - 1, 1).getTime() / 1000)
+    const startDate = new Date(now.getFullYear(), now.getMonth() - 1, 1)
     const end = Math.floor(new Date(now.getFullYear(), now.getMonth(), 1).getTime() / 1000) - 1
+    const start = Math.floor(startDate.getTime() / 1000)
     return {
       startTime: start,
       endTime: end,
-      label: '上个月',
+      label: `${startDate.getFullYear()}年${startDate.getMonth() + 1}月`,
       reason: '用户说“上个月”',
       source: 'query'
     }
   }
+  if (/今天/.test(query)) return fromQuery(dayStart(now), '今天', '用户说“今天”')
+  if (/昨天|前天/.test(query)) {
+    const daysAgo = query.includes('前天') ? 2 : 1
+    const start = dayStart(now) - daysAgo * 86400
+    return {
+      startTime: start,
+      endTime: dayStart(now) - (daysAgo - 1) * 86400 - 1,
+      label: daysAgo === 1 ? '昨天' : '前天',
+      reason: `用户说“${daysAgo === 1 ? '昨天' : '前天'}”`,
+      source: 'query'
+    }
+  }
+  if (/去年/.test(query)) {
+    const startDate = new Date(now.getFullYear() - 1, 0, 1)
+    return {
+      startTime: Math.floor(startDate.getTime() / 1000),
+      endTime: Math.floor(new Date(now.getFullYear(), 0, 1).getTime() / 1000) - 1,
+      label: `${startDate.getFullYear()}年`,
+      reason: '用户说“去年”',
+      source: 'query'
+    }
+  }
   if (/今年/.test(query)) return fromQuery(currentYearStart(now), '今年', '用户说“今年”')
   if (/最近/.test(query)) return fromQuery(nowSeconds - 30 * 86400, '近 30 天', '用户说“最近”')
   return {
@@ -456,6 +513,7 @@ export const inferAiSearchTimeRange = (
 }
 
 export const aiSearchIntentLabel = (intent: AiSearchIntent): string => {
+  if (intent === 'conversation_boundary') return '查询会话边界'
   if (intent === 'conversation_recall') return '回顾最近聊天'
   if (intent === 'conversation_topic_search') return '在指定聊天中查找话题'
   if (intent === 'global_sender_topic_search') return '按人物查找'
@@ -516,10 +574,27 @@ export const buildLocalAiSearchPlan = (
   query: string
 ): Pick<
   AiSearchPlan,
-  'intent' | 'keywords' | 'variants' | 'source' | 'contactQuery' | 'topicQuery'
+  'intent' | 'keywords' | 'variants' | 'source' | 'contactQuery' | 'topicQuery' | 'boundary' | 'projection'
 > => {
   const keywords = extractKeywords(query)
   const normalized = query.replace(/[“”"'‘’「」『』]/g, '').trim()
+  const boundary =
+    normalized.match(/^(?:我和|我跟|我与)\s*【?(.+?)】?\s*(第一次聊天|第一次说话|最早(?:一次)?(?:聊天|说话|聊过)?|最后一次聊天|最后一次说话|最近一次(?:聊天|说话))是什么时候[？?。！!]*$/) ||
+    normalized.match(/^我(最早|最后|最近一次)\s*什么时候\s*(?:和|跟|与)\s*【?(.+?)】?\s*(?:聊过|聊天|说话)[？?。！!]*$/) ||
+    normalized.match(/^我(最早|最后|最近一次)\s*(?:和|跟|与)\s*【?(.+?)】?\s*(?:聊过|聊天|说话)是什么时候[？?。！!]*$/)
+  if (boundary) {
+    const relationalFirst = /^(?:我和|我跟|我与)/.test(normalized)
+    const phrase = relationalFirst ? boundary[2] : boundary[1]
+    const first = /第一次|最早/.test(phrase)
+    const contact = (relationalFirst ? boundary[1] : boundary[2]).replace(/[【】]/g, '').trim()
+    const asksContent = /说了什么|说的什么|聊了什么|聊的什么/.test(normalized)
+    const asksTime = /什么时候|哪天/.test(normalized)
+    return {
+      intent: 'conversation_boundary', keywords: [], variants: [], source: 'local',
+      contactQuery: contact, boundary: first ? 'first' : 'last', topicQuery: undefined,
+      projection: asksContent && asksTime ? 'time_and_content' : asksContent ? 'content' : 'time'
+    }
+  }
   const recall = normalized.match(
     new RegExp(
       `(?:我和|我跟|我与)\\s*(.+?)\\s*(?:最近|这几天|本周|这个月|本月|今年|上个月|刚刚|刚才)?\\s*(?:${RECALL_QUESTION})`
@@ -603,7 +678,7 @@ export const parseAiSearchPlan = (
     const parsed = JSON.parse(jsonMatch[0]) as Record<string, unknown>
     const intent = [
       'general',
-      'conversation_recall',
+      'conversation_recall', 'conversation_boundary',
       'conversation_topic_search',
       'global_sender_topic_search',
       'global_group_topic_search',
@@ -626,15 +701,125 @@ export const parseAiSearchPlan = (
   }
 }
 
+const QUERY_UNDERSTANDING_INTENTS: AiSearchIntent[] = [
+  'conversation_recall',
+  'conversation_boundary',
+  'conversation_topic_search',
+  'global_sender_topic_search',
+  'global_group_topic_search',
+  'global_topic_search',
+  'conversation_name_search',
+  'general'
+]
+
+/** Strict host-side validation for the small semantic parser contract. */
+export const parseAiQueryUnderstanding = (value: string): AiQueryUnderstanding | null => {
+  const raw = value.trim()
+  if (!raw.startsWith('{') || !raw.endsWith('}')) return null
+  let parsed: Record<string, unknown>
+  try {
+    parsed = JSON.parse(raw) as Record<string, unknown>
+  } catch {
+    return null
+  }
+  const allowed = new Set([
+    'mode',
+    'intent',
+    'contactQuery',
+    'topicQuery',
+    'boundary',
+    'projection',
+    'targetQuery',
+    'semanticQuery',
+    'queryVariants',
+    'answerMode',
+    'confidence',
+    'requiresClarification',
+    'clarificationReason'
+  ])
+  if (Object.keys(parsed).some((key) => !allowed.has(key))) return null
+  const intent = String(parsed.intent) as AiSearchIntent
+  if (!QUERY_UNDERSTANDING_INTENTS.includes(intent)) return null
+  const mode = parsed.mode === null || parsed.mode === undefined ? 'structured' : parsed.mode
+  if (mode !== 'structured' && mode !== 'semantic' && mode !== 'clarification') return null
+  if (typeof parsed.confidence !== 'number' || !Number.isFinite(parsed.confidence)) return null
+  const confidence = Math.max(0, Math.min(1, parsed.confidence))
+  if (typeof parsed.requiresClarification !== 'boolean') return null
+  const textField = (key: 'contactQuery' | 'topicQuery'): string | undefined | null => {
+    const candidate = parsed[key]
+    if (candidate === null || candidate === undefined) return undefined
+    if (typeof candidate !== 'string') return null
+    const normalized = candidate.replace(/[【】]/g, '').trim()
+    if (!normalized || normalized.length > 64 || /conversationId|messageId|wxid_|SELECT\s|INSERT\s/i.test(normalized)) return null
+    return normalized
+  }
+  const contactQuery = textField('contactQuery')
+  const topicQuery = textField('topicQuery')
+  if (contactQuery === null || topicQuery === null) return null
+  const boundedField = (key: 'targetQuery' | 'semanticQuery', max: number): string | undefined | null => {
+    const candidate = parsed[key]
+    if (candidate === null || candidate === undefined) return undefined
+    if (typeof candidate !== 'string') return null
+    const normalized = candidate.trim()
+    if (!normalized || normalized.length > max || /conversationId|messageId|wxid_|SELECT\s|INSERT\s/i.test(normalized)) return null
+    return normalized
+  }
+  const targetQuery = boundedField('targetQuery', 64)
+  const semanticQuery = boundedField('semanticQuery', 200)
+  if (targetQuery === null || semanticQuery === null) return null
+  const queryVariants = parsed.queryVariants === null || parsed.queryVariants === undefined ? [] : parsed.queryVariants
+  if (!Array.isArray(queryVariants) || queryVariants.length > 4) return null
+  const normalizedVariants: string[] = []
+  for (const variant of queryVariants) {
+    if (typeof variant !== 'string') return null
+    const normalized = variant.trim()
+    if (!normalized || normalized.length > 80 || /conversationId|messageId|wxid_|SELECT\s|INSERT\s/i.test(normalized)) return null
+    normalizedVariants.push(normalized)
+  }
+  const answerMode = parsed.answerMode === null || parsed.answerMode === undefined ? undefined : parsed.answerMode
+  if (answerMode !== undefined && answerMode !== 'extract' && answerMode !== 'synthesis') return null
+  const boundary = parsed.boundary === null || parsed.boundary === undefined ? undefined : parsed.boundary
+  if (boundary !== undefined && boundary !== 'first' && boundary !== 'last') return null
+  const projection = parsed.projection === null || parsed.projection === undefined ? undefined : parsed.projection
+  if (projection !== undefined && projection !== 'time' && projection !== 'content' && projection !== 'time_and_content') return null
+  if (intent === 'conversation_boundary' && !boundary) return null
+  if (intent === 'conversation_boundary' && topicQuery !== undefined) return null
+  if (intent !== 'conversation_boundary' && boundary !== undefined) return null
+  if (intent !== 'conversation_boundary' && projection !== undefined) return null
+  if (intent === 'conversation_boundary' && !contactQuery) return null
+  if (mode === 'structured' && (targetQuery !== undefined || semanticQuery !== undefined || normalizedVariants.length || answerMode !== undefined)) return null
+  if (mode === 'semantic' && !semanticQuery) return null
+  if (mode === 'clarification' && !parsed.requiresClarification) return null
+  if (mode === 'semantic' && parsed.requiresClarification) return null
+  return {
+    mode,
+    intent,
+    contactQuery,
+    topicQuery,
+    boundary,
+    projection: intent === 'conversation_boundary' ? projection || 'time' : undefined,
+    targetQuery,
+    semanticQuery,
+    queryVariants: normalizedVariants,
+    answerMode: mode === 'semantic' ? answerMode || 'synthesis' : undefined,
+    confidence,
+    requiresClarification: parsed.requiresClarification,
+    clarificationReason:
+      typeof parsed.clarificationReason === 'string'
+        ? parsed.clarificationReason.slice(0, 160)
+        : undefined
+  }
+}
+
 export const mergeAiSearchPlans = (
   local: Pick<
     AiSearchPlan,
-    'intent' | 'keywords' | 'variants' | 'source' | 'contactQuery' | 'topicQuery'
+    'intent' | 'keywords' | 'variants' | 'source' | 'contactQuery' | 'topicQuery' | 'boundary' | 'projection'
   >,
-  ai: Partial<Pick<AiSearchPlan, 'intent' | 'keywords' | 'variants' | 'topicQuery'>> | null
+  ai: Partial<Pick<AiSearchPlan, 'intent' | 'keywords' | 'variants' | 'topicQuery' | 'projection'>> | null
 ): Pick<
   AiSearchPlan,
-  'intent' | 'keywords' | 'variants' | 'source' | 'contactQuery' | 'topicQuery'
+  'intent' | 'keywords' | 'variants' | 'source' | 'contactQuery' | 'topicQuery' | 'boundary' | 'projection'
 > => {
   if (!ai) return local
   const keywords = normalizeTerms([...local.keywords, ...(ai.keywords || [])])
@@ -657,7 +842,9 @@ export const mergeAiSearchPlans = (
     variants,
     source: 'hybrid',
     contactQuery: local.contactQuery,
-    topicQuery: local.topicQuery || ai.topicQuery
+    topicQuery: local.topicQuery || ai.topicQuery,
+    boundary: local.boundary,
+    projection: local.projection || ai.projection
   }
 }
 

src/shared/knowledge.ts

@@ -7,7 +7,7 @@
 export const KNOWLEDGE_SCHEMA_VERSION = 1
 export const DEFAULT_CHUNKER_VERSION = 'conversation-v1'
 
-export type KnowledgeMessageKind = 'text' | 'voice' | 'file' | 'link' | 'system' | 'other'
+export type KnowledgeMessageKind = 'text' | 'voice' | 'file' | 'link' | 'image' | 'video' | 'sticker' | 'system' | 'other'
 export type KnowledgeIndexPhase =
   | 'idle'
   | 'preflight'
@@ -210,6 +210,7 @@ export interface KnowledgeQuery {
   /** Unix epoch milliseconds. */
   endTime?: number
   temporalIntent?: KnowledgeTemporalIntent
+  conversationBoundary?: 'first' | 'last'
 }
 
 export interface KnowledgeSearchRequest extends KnowledgeQuery {
@@ -299,6 +300,7 @@ export interface KnowledgeSearchIpcRequest {
   senderIds?: string[]
   startTime?: number
   endTime?: number
+  conversationBoundary?: 'first' | 'last'
   limit?: number
 }
 

tests/unit/ai-search-pipeline-service.test.ts

@@ -10,7 +10,8 @@ vi.mock('../../src/main/services/chat-service', () => ({
   listContactsAsync
 }))
 
-import { AiSearchPipelineService } from '../../src/main/services/ai-search-pipeline-service'
+import { AiSearchPipelineService, isSuspiciousLocalPlan } from '../../src/main/services/ai-search-pipeline-service'
+import { buildLocalAiSearchPlan } from '../../src/shared/ai-search'
 import type { KnowledgeEvidence } from '../../src/shared/knowledge'
 
 const makeCandidate = (index: number): KnowledgeEvidence => ({
@@ -143,6 +144,331 @@ describe('AiSearchPipelineService', () => {
     expect(result.agent).toMatchObject({ mode: 'agent', toolCalls: 1 })
   })
 
+  it('uses the AI query-understanding fallback for the real 小史 boundary expression', async () => {
+    listContactsAsync.mockResolvedValue([
+      { md5: 'xiaoshi', m_nsUsrName: 'wxid_xiaoshi', m_nsNickName: '小史', type: 'user' }
+    ])
+    aiProvider.chat.mockReset()
+    aiProvider.chat.mockResolvedValueOnce({
+      success: true,
+      data: JSON.stringify({
+        intent: 'conversation_boundary', contactQuery: '小史', topicQuery: null,
+        boundary: 'first', confidence: 0.98, requiresClarification: false
+      })
+    })
+    knowledge.search.mockResolvedValue({
+      source: 'knowledge', state: 'ready', indexedMessageCount: 2,
+      indexedChunkCount: 1, totalMessages: 2,
+      evidence: [{
+        chunkId: 'boundary', conversationId: 'xiaoshi', startTime: 1, endTime: 1,
+        messageId: 'message-1', sender: '小史', senderId: 'wxid_xiaoshi', timestamp: 1,
+        messageIds: ['message-1'], sourceKind: 'text', text: '你好'
+      }],
+      conversationRetrieval: {
+        conversationId: 'xiaoshi', totalMessages: 2, chunkCount: 1,
+        candidateMessages: 1, systemMessagesDeprioritized: 0, complete: true
+      }
+    })
+    const service = new AiSearchPipelineService(knowledge as never, aiProvider as never)
+    const localPlan = buildLocalAiSearchPlan('我和小史第一次聊天是在什么时候')
+    expect(localPlan).toMatchObject({ intent: 'global_topic_search' })
+    expect(localPlan.contactQuery).toBeUndefined()
+    expect(localPlan.boundary).toBeUndefined()
+    expect(isSuspiciousLocalPlan('我和小史第一次聊天是在什么时候', localPlan as never)).toBe(true)
+    const result = await service.run(
+      { requestId: 'ai-boundary', text: '我和小史第一次聊天是在什么时候', scope: 'global', range: 'all' },
+      () => undefined
+    )
+    expect(aiProvider.chat).toHaveBeenCalledTimes(1)
+    const parserMessages = aiProvider.chat.mock.calls[0][0] as Array<{ role: string; content: string }>
+    expect(parserMessages[1].content).toContain('我和小史第一次聊天是在什么时候')
+    expect(parserMessages[1].content).toContain('当前界面范围：global')
+    expect(parserMessages[1].content).not.toMatch(/Evidence|conversationId|wxid_|knowledge|contacts|数据库|路径/)
+    expect(knowledge.search).toHaveBeenCalledWith(expect.objectContaining({
+      conversationIds: ['xiaoshi'], conversationBoundary: 'first', terms: []
+    }))
+    expect(result).toMatchObject({ status: 'completed', plan: {
+      intent: 'conversation_boundary', contactQuery: '小史', boundary: 'first'
+    }, retrieval: { identityResolution: 'resolved', conversationId: 'xiaoshi' }, answer: expect.stringContaining('[E1]'), evidence: [expect.any(Object)] })
+    expect(result.agent).toMatchObject({ toolCalls: 0 })
+  })
+
+  it('supports content projection through the real BOBO boundary pipeline', async () => {
+    listContactsAsync.mockResolvedValue([
+      { md5: 'bobo', m_nsUsrName: 'wxid_bobo', m_nsNickName: 'BOBO', type: 'user' }
+    ])
+    aiProvider.chat.mockReset()
+    aiProvider.chat.mockResolvedValueOnce({
+      success: true,
+      data: JSON.stringify({
+        intent: 'conversation_boundary', contactQuery: 'BOBO', topicQuery: null,
+        boundary: 'first', projection: 'content', confidence: 0.98, requiresClarification: false
+      })
+    })
+    knowledge.search.mockResolvedValue({
+      source: 'knowledge', state: 'ready', indexedMessageCount: 3,
+      indexedChunkCount: 1, totalMessages: 3,
+      evidence: [{
+        chunkId: 'bobo-boundary', conversationId: 'bobo', startTime: 2, endTime: 2,
+        messageId: 'bobo-1', sender: 'BOBO', senderId: 'wxid_bobo', timestamp: 2,
+        messageIds: ['bobo-1'], sourceKind: 'voice', text: '语音转写：你好'
+      }],
+      conversationRetrieval: {
+        conversationId: 'bobo', totalMessages: 3, chunkCount: 1,
+        candidateMessages: 1, systemMessagesDeprioritized: 0, complete: true
+      }
+    })
+    const result = await new AiSearchPipelineService(knowledge as never, aiProvider as never).run(
+      { requestId: 'bobo-content', text: '我和BOBO第一次讲话说的什么', scope: 'global', range: 'all' },
+      () => undefined
+    )
+    expect(aiProvider.chat).toHaveBeenCalledTimes(1)
+    expect(knowledge.search).toHaveBeenCalledWith(expect.objectContaining({
+      conversationIds: ['bobo'], conversationBoundary: 'first', terms: []
+    }))
+    expect(result).toMatchObject({
+      status: 'completed',
+      plan: { intent: 'conversation_boundary', contactQuery: 'BOBO', boundary: 'first', projection: 'content' },
+      answer: expect.stringContaining('语音转写：你好'),
+      evidence: [expect.any(Object)]
+    })
+    expect(result.answer).not.toContain('正在生成')
+  })
+
+  it('executes a semantic retrieval plan with bounded probes and one answer AI call', async () => {
+    listContactsAsync.mockResolvedValue([
+      { md5: 'bobo', m_nsUsrName: 'wxid_bobo', m_nsNickName: 'BOBO', type: 'user' }
+    ])
+    aiProvider.chat.mockReset()
+    aiProvider.chat
+      .mockResolvedValueOnce({
+        success: true,
+        data: JSON.stringify({
+          mode: 'semantic', intent: 'general', targetQuery: 'BOBO',
+          semanticQuery: '双方关系开始明显变熟、互动增加或开始更深入交流',
+          queryVariants: ['开始熟起来', '关系变熟', '聊天明显增多', '开始聊更深入的话题'],
+          answerMode: 'synthesis', confidence: 0.97, requiresClarification: false
+        })
+      })
+      .mockResolvedValueOnce({ success: true, data: 'BOBO后来逐渐和我熟悉起来。[E1]' })
+    knowledge.search.mockImplementation(async (query: { terms: string[] }) => ({
+      source: 'knowledge', state: 'ready', indexedMessageCount: 10,
+      indexedChunkCount: 4, totalMessages: 10,
+      evidence: [{
+        chunkId: `semantic-${query.terms[0]}`, conversationId: 'bobo', startTime: 2, endTime: 2,
+        messageId: `message-${query.terms[0]}`, sender: 'BOBO', senderId: 'wxid_bobo', timestamp: 2,
+        messageIds: [`message-${query.terms[0]}`], sourceKind: 'text', text: `关于${query.terms[0]}的聊天`
+      }]
+    }))
+    const result = await new AiSearchPipelineService(knowledge as never, aiProvider as never).run(
+      { requestId: 'semantic-bobo', text: 'BOBO什么时候开始跟我熟起来的', scope: 'global', range: 'all' },
+      () => undefined
+    )
+    expect(result).toMatchObject({
+      status: 'completed',
+      plan: {
+        mode: 'semantic', targetQuery: 'BOBO', contactQuery: 'BOBO',
+        semanticQuery: '双方关系开始明显变熟、互动增加或开始更深入交流',
+        queryVariants: ['开始熟起来', '关系变熟', '聊天明显增多', '开始聊更深入的话题'],
+        answerMode: 'synthesis'
+      },
+      retrieval: { identityResolution: 'resolved', conversationId: 'bobo' },
+      answer: expect.stringContaining('BOBO后来逐渐和我熟悉起来。[E1]')
+    })
+    expect(knowledge.search).toHaveBeenCalledTimes(5)
+    expect(knowledge.search.mock.calls.map(([query]) => query.terms)).toEqual([
+      ['双方关系开始明显变熟、互动增加或开始更深入交流'], ['开始熟起来'], ['关系变熟'],
+      ['聊天明显增多'], ['开始聊更深入的话题']
+    ])
+    expect(aiProvider.chat).toHaveBeenCalledTimes(2)
+  })
+
+  it('keeps promise-like natural language in semantic retrieval instead of understanding_failed', async () => {
+    listContactsAsync.mockResolvedValue([
+      { md5: 'laowang', m_nsUsrName: 'wxid_laowang', m_nsNickName: '老王', type: 'user' }
+    ])
+    aiProvider.chat.mockReset()
+    aiProvider.chat
+      .mockResolvedValueOnce({
+        success: true,
+        data: JSON.stringify({
+          mode: 'semantic', intent: 'general', targetQuery: '老王',
+          semanticQuery: '承诺之后提供、发送或完成某件事情', queryVariants: ['我给你', '我发你', '答应'],
+          answerMode: 'synthesis', confidence: 0.94, requiresClarification: false
+        })
+      })
+      .mockResolvedValueOnce({ success: true, data: '老王答应过随后把东西发给我。[E1]' })
+    knowledge.search.mockResolvedValue({
+      source: 'knowledge', state: 'ready', indexedMessageCount: 4,
+      indexedChunkCount: 1, totalMessages: 4,
+      evidence: [{
+        chunkId: 'promise', conversationId: 'laowang', startTime: 3, endTime: 3,
+        messageId: 'promise-1', sender: '老王', senderId: 'wxid_laowang', timestamp: 3,
+        messageIds: ['promise-1'], sourceKind: 'text', text: '我发你，答应'
+      }]
+    })
+    const result = await new AiSearchPipelineService(knowledge as never, aiProvider as never).run(
+      { requestId: 'semantic-laowang', text: '老王之前答应我的东西是什么', scope: 'global', range: 'all' },
+      () => undefined
+    )
+    expect(result.status).toBe('completed')
+    expect(result.plan).toMatchObject({ mode: 'semantic', targetQuery: '老王', contactQuery: '老王' })
+    expect(result.retrieval).toMatchObject({ identityResolution: 'resolved', conversationId: 'laowang' })
+    expect(aiProvider.chat).toHaveBeenCalledTimes(2)
+  })
+
+  it.each([
+    ['我和张三最近聊了什么', 'conversation_recall'],
+    ['我和张三第一次聊天是什么时候', 'conversation_boundary'],
+    ['我和张三第一次聊天是在什么时候', 'conversation_boundary'],
+    ['我和张三第一次说话是哪天', 'conversation_boundary'],
+    ['我第一次跟张三聊天是什么时候', 'conversation_boundary'],
+    ['我最早什么时候和张三聊过', 'conversation_boundary'],
+    ['我和张三是从什么时候开始聊天的', 'conversation_boundary'],
+    ['我和张三什么时候开始聊天的', 'conversation_boundary'],
+    ['我和张三最后一次聊天是什么时候', 'conversation_boundary'],
+    ['我和张三最后一次聊天是在什么时候', 'conversation_boundary'],
+    ['我最近一次跟张三说话是什么时候', 'conversation_boundary'],
+    ['我上一次和张三聊天是哪天', 'conversation_boundary'],
+    ['张三最后一次和我聊天是在什么时候', 'conversation_boundary'],
+    ['我和张三最近聊得怎么样', 'not_boundary'],
+    ['我和张三聊过装修吗', 'not_boundary'],
+    ['最近张三说了什么', 'not_boundary']
+  ])('keeps the boundary synonym/negative matrix out of accidental intent: %s', (query, expected) => {
+    const plan = buildLocalAiSearchPlan(query)
+    if (expected === 'conversation_recall') expect(plan.intent).toBe('conversation_recall')
+    else if (expected === 'conversation_boundary') {
+      expect(plan.intent === 'conversation_boundary' || plan.intent === 'global_topic_search').toBe(true)
+    } else expect(plan.intent).not.toBe('conversation_boundary')
+  })
+
+  it.each([
+    'BOBO什么时候开始跟我熟起来的',
+    '我跟BOBO刚认识的时候聊了什么',
+    '老王之前答应我的东西是什么',
+    '我之前是不是跟谁提过买房'
+  ])('routes interpretive natural language to the Query Compiler: %s', (query) => {
+    const plan = buildLocalAiSearchPlan(query)
+    expect(isSuspiciousLocalPlan(query, plan as never)).toBe(true)
+  })
+
+  it.each([
+    ['我和张三第一次聊天是什么时候', 'first'],
+    ['我和张三第一次聊天是在什么时候', 'first'],
+    ['我和张三第一次说话是哪天', 'first'],
+    ['我第一次跟张三聊天是什么时候', 'first'],
+    ['我最早什么时候和张三聊过', 'first'],
+    ['我和张三是从什么时候开始聊天的', 'first'],
+    ['我和张三什么时候开始聊天的', 'first'],
+    ['我和张三最后一次聊天是什么时候', 'last'],
+    ['我和张三最后一次聊天是在什么时候', 'last'],
+    ['我最近一次跟张三说话是什么时候', 'last'],
+    ['我上一次和张三聊天是哪天', 'last'],
+    ['张三最后一次和我聊天是在什么时候', 'last']
+  ])('normalizes the full boundary synonym matrix through the Pipeline: %s', async (query, boundary) => {
+    listContactsAsync.mockResolvedValue([
+      { md5: 'zhangsan', m_nsUsrName: 'wxid_zhangsan', m_nsNickName: '张三', type: 'user' }
+    ])
+    aiProvider.getRuntimeConfig.mockReturnValue({ configured: true, providerId: 'fixture-provider', model: 'fixture-model' })
+    aiProvider.getAiSearchProviderStatus.mockReturnValue({ configured: true, requiresConsent: false, providerId: 'fixture-provider', recipient: 'fixture' })
+    aiProvider.chat.mockReset()
+    aiProvider.chat.mockResolvedValueOnce({
+      success: true,
+      data: JSON.stringify({ intent: 'conversation_boundary', contactQuery: '张三', topicQuery: null, boundary, confidence: 0.98, requiresClarification: false })
+    })
+    knowledge.search.mockResolvedValue({
+      source: 'knowledge', state: 'ready', indexedMessageCount: 1, indexedChunkCount: 1, totalMessages: 1,
+      evidence: [{ chunkId: 'boundary', conversationId: 'zhangsan', startTime: 1, endTime: 1, messageId: 'message-1', sender: '张三', senderId: 'wxid_zhangsan', timestamp: 1, messageIds: ['message-1'], sourceKind: 'text', text: '你好' }],
+      conversationRetrieval: { conversationId: 'zhangsan', totalMessages: 1, chunkCount: 1, candidateMessages: 1, systemMessagesDeprioritized: 0, complete: true }
+    })
+    const result = await new AiSearchPipelineService(knowledge as never, aiProvider as never).run(
+      { requestId: `matrix-${boundary}-${query}`, text: query, scope: 'global', range: 'all' },
+      () => undefined
+    )
+    const local = buildLocalAiSearchPlan(query)
+    const expectedParserCalls = local.intent === 'conversation_boundary' ? 0 : 1
+    expect(result).toMatchObject({ status: 'completed', plan: { intent: 'conversation_boundary', contactQuery: '张三', boundary }, retrieval: { identityResolution: 'resolved' }, evidence: [expect.any(Object)] })
+    expect(aiProvider.chat).toHaveBeenCalledTimes(expectedParserCalls)
+    expect(knowledge.search).toHaveBeenCalledWith(expect.objectContaining({ conversationIds: ['zhangsan'], terms: [], conversationBoundary: boundary }))
+  })
+
+  it('returns understanding_failed for unavailable and throwing Query Parser fallback', async () => {
+    listContactsAsync.mockResolvedValue([
+      { md5: 'zhangsan', m_nsUsrName: 'wxid_zhangsan', m_nsNickName: '张三', type: 'user' }
+    ])
+    aiProvider.getRuntimeConfig.mockReturnValue({ configured: false })
+    aiProvider.getAiSearchProviderStatus.mockReturnValue({ configured: false })
+    const service = new AiSearchPipelineService(knowledge as never, aiProvider as never)
+    const unavailable = await service.run(
+      { requestId: 'understanding-unavailable', text: '我和张三第一次聊天是在什么时候', scope: 'global', range: 'all' },
+      () => undefined
+    )
+    expect(unavailable).toMatchObject({ status: 'understanding_failed' })
+    expect(knowledge.search).not.toHaveBeenCalled()
+
+    aiProvider.getRuntimeConfig.mockReturnValue({ configured: true, providerId: 'fixture-provider', model: 'fixture-model' })
+    aiProvider.getAiSearchProviderStatus.mockReturnValue({ configured: true, requiresConsent: false, providerId: 'fixture-provider', recipient: 'fixture' })
+    aiProvider.chat.mockReset()
+    aiProvider.chat.mockRejectedValueOnce(new Error('Query Parser timeout'))
+    const throwing = await service.run(
+      { requestId: 'understanding-timeout', text: '我和张三第一次聊天是在什么时候', scope: 'global', range: 'all' },
+      () => undefined
+    )
+    expect(throwing).toMatchObject({ status: 'understanding_failed', error: expect.stringContaining('Query Parser timeout') })
+    expect(knowledge.search).not.toHaveBeenCalled()
+  })
+
+  it('keeps local recall and local boundary at zero Query Parser calls when AI is unavailable', async () => {
+    listContactsAsync.mockResolvedValue([
+      { md5: 'zhangsan', m_nsUsrName: 'wxid_zhangsan', m_nsNickName: '张三', type: 'user' }
+    ])
+    aiProvider.getRuntimeConfig.mockReturnValue({ configured: false })
+    aiProvider.getAiSearchProviderStatus.mockReturnValue({ configured: false })
+    knowledge.search.mockResolvedValue({ source: 'knowledge', state: 'ready', indexedMessageCount: 1, indexedChunkCount: 1, totalMessages: 1, evidence: [] })
+    const service = new AiSearchPipelineService(knowledge as never, aiProvider as never)
+    const recall = await service.run({ requestId: 'local-recall-no-ai', text: '我和张三最近聊了什么', scope: 'global', range: 'all' }, () => undefined)
+    expect(recall.plan.intent).toBe('conversation_recall')
+    expect(aiProvider.chat).toHaveBeenCalledTimes(0)
+    aiProvider.chat.mockReset()
+    const boundary = await service.run({ requestId: 'local-boundary-no-ai', text: '我和张三第一次聊天是什么时候', scope: 'global', range: 'all' }, () => undefined)
+    expect(boundary.plan.intent).toBe('conversation_boundary')
+    expect(aiProvider.chat).toHaveBeenCalledTimes(0)
+  })
+
+  it('stops before Knowledge when contact resolution is ambiguous or not found', async () => {
+    const parser = JSON.stringify({ intent: 'conversation_boundary', contactQuery: '张三', topicQuery: null, boundary: 'first', confidence: 0.98, requiresClarification: false })
+    aiProvider.chat.mockReset()
+    aiProvider.chat.mockResolvedValue({ success: true, data: parser })
+    aiProvider.getRuntimeConfig.mockReturnValue({ configured: true, providerId: 'fixture-provider', model: 'fixture-model' })
+    aiProvider.getAiSearchProviderStatus.mockReturnValue({ configured: true, requiresConsent: false, providerId: 'fixture-provider', recipient: 'fixture' })
+    const service = new AiSearchPipelineService(knowledge as never, aiProvider as never)
+    listContactsAsync.mockResolvedValue([
+      { md5: 'zhangsan-a', m_nsUsrName: 'wxid_a', m_nsNickName: '张三', type: 'user' },
+      { md5: 'zhangsan-b', m_nsUsrName: 'wxid_b', m_nsNickName: '张三', type: 'user' }
+    ])
+    const ambiguous = await service.run({ requestId: 'ambiguous-boundary', text: '我和张三第一次聊天是在什么时候', scope: 'global', range: 'all' }, () => undefined)
+    expect(ambiguous).toMatchObject({ status: 'ambiguous_contact' })
+    expect(knowledge.search).not.toHaveBeenCalled()
+
+    aiProvider.chat.mockReset()
+    aiProvider.chat.mockResolvedValueOnce({ success: true, data: parser.replace('张三', '不存在的人') })
+    listContactsAsync.mockResolvedValue([{ md5: 'other', m_nsUsrName: 'wxid_other', m_nsNickName: '其他人', type: 'user' }])
+    const missing = await service.run({ requestId: 'missing-boundary', text: '我和不存在的人第一次聊天是在什么时候', scope: 'global', range: 'all' }, () => undefined)
+    expect(missing).toMatchObject({ status: 'contact_not_found' })
+    expect(knowledge.search).not.toHaveBeenCalled()
+  })
+
+  it('distinguishes a confirmed contact with no readable messages', async () => {
+    listContactsAsync.mockResolvedValue([{ md5: 'zhangsan', m_nsUsrName: 'wxid_zhangsan', m_nsNickName: '张三', type: 'user' }])
+    knowledge.search.mockResolvedValue({ source: 'knowledge', state: 'ready', indexedMessageCount: 1, indexedChunkCount: 1, totalMessages: 1, evidence: [], conversationRetrieval: { conversationId: 'zhangsan', totalMessages: 1, chunkCount: 1, candidateMessages: 0, systemMessagesDeprioritized: 1, complete: true } })
+    aiProvider.getRuntimeConfig.mockReturnValue({ configured: false })
+    aiProvider.getAiSearchProviderStatus.mockReturnValue({ configured: false })
+    const service = new AiSearchPipelineService(knowledge as never, aiProvider as never)
+    const result = await service.run({ requestId: 'no-boundary-messages', text: '我和张三第一次聊天是什么时候', scope: 'global', range: 'all' }, () => undefined)
+    expect(result).toMatchObject({ status: 'no_messages' })
+    expect(knowledge.search).toHaveBeenCalledWith(expect.objectContaining({ conversationBoundary: 'first' }))
+  })
+
   it('cancels an active Agent request and aborts the AI call before local retrieval continues', async () => {
     let observedSignal: AbortSignal | undefined
     let markStarted: (() => void) | undefined

tests/unit/ai-search-time-range.test.ts

@@ -27,6 +27,27 @@ describe('AI search natural-language time ranges', () => {
     })
   })
 
+  it.each([
+    ['我和BOBO上个月聊了什么', '2026-08-01T00:00:00+08:00', '2026-08-31T23:59:59+08:00'],
+    ['我和BOBO这个月聊了什么', '2026-09-01T00:00:00+08:00', undefined],
+    ['我和BOBO昨天聊了什么', '2026-09-08T00:00:00+08:00', '2026-09-08T23:59:59+08:00'],
+    ['我和BOBO前天聊了什么', '2026-09-07T00:00:00+08:00', '2026-09-07T23:59:59+08:00'],
+    ['我和BOBO去年聊了什么', '2025-01-01T00:00:00+08:00', '2025-12-31T23:59:59+08:00']
+  ])('%s resolves against the injected clock', (query, start, end) => {
+    const range = inferAiSearchTimeRange(query, 'all', new Date('2026-09-09T15:00:00+08:00'))
+    expect(range.startTime).toBe(Math.floor(new Date(start).getTime() / 1000))
+    expect(range.endTime).toBe(end ? Math.floor(new Date(end).getTime() / 1000) : Math.floor(new Date('2026-09-09T15:00:00+08:00').getTime() / 1000))
+  })
+
+  it.each([
+    ['2026-01-10T12:00:00+08:00', '2025-12-01T00:00:00+08:00', '2025-12-31T23:59:59+08:00'],
+    ['2026-03-01T12:00:00+08:00', '2026-02-01T00:00:00+08:00', '2026-02-28T23:59:59+08:00']
+  ])('handles previous-month year and month boundaries from %s', (now, start, end) => {
+    const range = inferAiSearchTimeRange('我和BOBO上个月聊了什么', 'all', new Date(now))
+    expect(range.startTime).toBe(Math.floor(new Date(start).getTime() / 1000))
+    expect(range.endTime).toBe(Math.floor(new Date(end).getTime() / 1000))
+  })
+
   it('keeps an explicit user retry override above the word 最近 in the original question', () => {
     expect(
       inferAiSearchTimeRange('我和张三最近聊了什么？', 'all', NOW, {

tests/unit/ai-search-workspace-pure-functions.test.ts

@@ -99,6 +99,28 @@ const makeTrace = (overrides: Partial<SearchTrace> = {}): SearchTrace => ({
 })
 
 describe('AI Search request context', () => {
+  it('includes the resolved absolute range in relative-time cache keys', () => {
+    const query = '我和BOBO上个月聊了什么'
+    const first = createSearchRequestContext({
+      query, scope: 'global', range: 'all', now: new Date('2025-09-09T12:00:00+08:00')
+    })
+    const second = createSearchRequestContext({
+      query, scope: 'global', range: 'all', now: new Date('2026-09-09T12:00:00+08:00')
+    })
+    const repeat = createSearchRequestContext({
+      query, scope: 'global', range: 'all', now: new Date('2026-09-09T12:00:00+08:00')
+    })
+    const refreshed = createSearchRequestContext({
+      query, scope: 'global', range: 'all', now: new Date('2026-09-09T12:00:00+08:00'),
+      knowledgeGeneration: 'ready:200:20:200'
+    })
+    expect(first.resolvedTimeRange.label).toBe('2025年8月')
+    expect(second.resolvedTimeRange.label).toBe('2026年8月')
+    expect(second.cacheKey).not.toBe(first.cacheKey)
+    expect(second.cacheKey).toBe(repeat.cacheKey)
+    expect(refreshed.cacheKey).not.toBe(second.cacheKey)
+  })
+
   it('trims only the submitted query while keeping cache query normalization unchanged', () => {
     const context = createSearchRequestContext({
       query: '  Mixed  Case 问题  ',
@@ -107,7 +129,7 @@ describe('AI Search request context', () => {
     })
 
     expect(context.normalizedQuery).toBe('Mixed  Case 问题')
-    expect(context.cacheKey).toBe(JSON.stringify(['global', '', '30d', 'mixed  case 问题']))
+    expect(JSON.parse(context.cacheKey).slice(0, 4)).toEqual(['global', '', '30d', 'mixed  case 问题'])
   })
 
   it.each(['global', 'groups', 'contacts'] as const)(
@@ -121,7 +143,7 @@ describe('AI Search request context', () => {
       })
 
       expect(context.conversationId).toBeUndefined()
-      expect(context.cacheKey).toBe(JSON.stringify([scope, '', '7d', '范围问题']))
+      expect(JSON.parse(context.cacheKey).slice(0, 4)).toEqual([scope, '', '7d', '范围问题'])
     }
   )
 
@@ -134,8 +156,8 @@ describe('AI Search request context', () => {
     })
 
     expect(context.conversationId).toBe(aiSearchContact.md5)
-    expect(context.cacheKey).toBe(
+    expect(JSON.parse(context.cacheKey).slice(0, 4)).toEqual(
-      JSON.stringify(['conversation', aiSearchContact.md5, 'today', '会话问题'])
+      ['conversation', aiSearchContact.md5, 'today', '会话问题']
     )
   })
 
@@ -147,7 +169,7 @@ describe('AI Search request context', () => {
     })
 
     expect(context.conversationId).toBeUndefined()
-    expect(context.cacheKey).toBe(JSON.stringify(['conversation', '', 'all', '未选择会话']))
+    expect(JSON.parse(context.cacheKey).slice(0, 4)).toEqual(['conversation', '', 'all', '未选择会话'])
   })
 
   it('uses retry range and retry time override when both are provided', () => {
@@ -171,7 +193,7 @@ describe('AI Search request context', () => {
 
     expect(context.effectiveRange).toBe('all')
     expect(context.effectiveTimeRangeOverride).toBe(retryOverride)
-    expect(context.cacheKey).toBe(JSON.stringify(['global', '', 'all', '重试问题']))
+    expect(JSON.parse(context.cacheKey).slice(0, 4)).toEqual(['global', '', 'all', '重试问题'])
   })
 
   it('falls back to the current time override when retry does not provide one', () => {
@@ -386,6 +408,23 @@ describe('AI Search trace and cache mapping', () => {
     })
   })
 
+  it('reports conversation recall coverage instead of keyword-hit count', () => {
+    const result = makeSearchResult()
+    result.candidateEvidenceCount = 8
+    result.retrieval = {
+      intent: 'conversation_recall',
+      sourceMessageCount: 31,
+      sourceCoverage: 'complete',
+      isComplete: true,
+      candidateCount: 8,
+      uniqueCandidateCount: 8,
+      fallbackUsed: false,
+      suspicious: false,
+      timeRange: { label: '2026年8月', reason: 'test', source: 'query' }
+    }
+    expect(mapSearchResultToTrace(result, 8).retrievedEvidence).toBe(31)
+  })
+
   it('maps AI token, citation, and voice coverage details without transforming them', () => {
     const result: AiSearchPipelineResult = makeSearchResult()
     result.ai = {
@@ -560,6 +599,10 @@ describe('AI Search result view transition', () => {
   })
 
   it.each([
+    ['understanding_failed', 'insufficient', '我没有完全理解你想怎么查，可以换一种说法。'],
+    ['contact_not_found', 'insufficient', '我理解你在问这个联系人，但没有在当前通讯录中确认到对应联系人。'],
+    ['ambiguous_contact', 'insufficient', '找到多个可能的联系人，暂时无法确定你指的是哪一个。'],
+    ['no_messages', 'insufficient', '已经确认联系人，但当前可读取记录里没有对应聊天消息。'],
     ['retrieval_incomplete', 'partial', '当前检索未完整覆盖聊天记录，未生成总结。'],
     ['failed', 'insufficient', '本地搜索暂时无法完成'],
     ['ai_failed', 'partial', '证据已找到，但 AI 暂时无法生成回答']

tests/unit/conversation-boundary.test.ts

@@ -0,0 +1,119 @@
+import { mkdtempSync } from 'fs'
+import { rm } from 'fs/promises'
+import { tmpdir } from 'os'
+import { join } from 'path'
+import { afterEach, describe, expect, it } from 'vitest'
+import { buildLocalAiSearchPlan, parseAiQueryUnderstanding } from '../../src/shared/ai-search'
+import { DEFAULT_KNOWLEDGE_CHUNKER, type KnowledgeFtsConfig } from '../../src/shared/knowledge'
+import { KnowledgeStore } from '../../src/main/knowledge/knowledge-store'
+
+const roots: string[] = []
+const fts: KnowledgeFtsConfig = {
+  profileId: 'boundary-test', tokenizer: 'trigram', contentMode: 'external', detail: 'full', columnsize: 1
+}
+
+afterEach(async () => {
+  await Promise.all(roots.splice(0).map((root) => rm(root, { recursive: true, force: true })))
+})
+
+describe('conversation boundary query planning', () => {
+  it('validates the AI query understanding contract without accepting unsafe fields', () => {
+    expect(parseAiQueryUnderstanding(JSON.stringify({
+      intent: 'conversation_boundary', contactQuery: '小史', topicQuery: null,
+      boundary: 'first', confidence: 0.98, requiresClarification: false
+    }))).toMatchObject({ intent: 'conversation_boundary', contactQuery: '小史', boundary: 'first' })
+    expect(parseAiQueryUnderstanding('```json\n{"intent":"conversation_boundary"}\n```')).toBeNull()
+    expect(parseAiQueryUnderstanding('{"intent":"conversation_boundary"}{"intent":"general"}')).toBeNull()
+    expect(parseAiQueryUnderstanding(JSON.stringify({ intent: 'deep_research', confidence: 1, requiresClarification: false }))).toBeNull()
+    expect(parseAiQueryUnderstanding(JSON.stringify({ intent: 'conversation_boundary', contactQuery: '张三', boundary: 'middle', confidence: 1, requiresClarification: false }))).toBeNull()
+    expect(parseAiQueryUnderstanding(JSON.stringify({ intent: 'conversation_boundary', contactQuery: '张三'.repeat(33), boundary: 'first', confidence: 1, requiresClarification: false }))).toBeNull()
+    expect(parseAiQueryUnderstanding(JSON.stringify({ intent: 'conversation_boundary', contactQuery: '张三', confidence: 1, requiresClarification: false }))).toBeNull()
+    expect(parseAiQueryUnderstanding(JSON.stringify({ intent: 'conversation_boundary', contactQuery: '张三', topicQuery: '装修', boundary: 'first', confidence: 1, requiresClarification: false }))).toBeNull()
+    expect(parseAiQueryUnderstanding(JSON.stringify({ intent: 'conversation_recall', contactQuery: '张三', boundary: 'first', confidence: 1, requiresClarification: false }))).toBeNull()
+    expect(parseAiQueryUnderstanding(JSON.stringify({
+      intent: 'conversation_boundary', contactQuery: '张三', boundary: 'first', projection: 'content',
+      confidence: 1, requiresClarification: false
+    }))).toMatchObject({ projection: 'content' })
+    expect(parseAiQueryUnderstanding(JSON.stringify({
+      intent: 'conversation_boundary', contactQuery: '张三', boundary: 'first', projection: 'middle',
+      confidence: 1, requiresClarification: false
+    }))).toBeNull()
+    expect(parseAiQueryUnderstanding(JSON.stringify({
+      intent: 'conversation_recall', contactQuery: '张三', projection: 'content',
+      confidence: 1, requiresClarification: false
+    }))).toBeNull()
+    expect(parseAiQueryUnderstanding(JSON.stringify({
+      mode: 'semantic', intent: 'general', targetQuery: 'BOBO',
+      semanticQuery: '双方关系开始明显变熟、互动增加或开始更深入交流',
+      queryVariants: ['开始熟起来', '关系变熟'], answerMode: 'synthesis',
+      confidence: 0.95, requiresClarification: false
+    }))).toMatchObject({ mode: 'semantic', targetQuery: 'BOBO', answerMode: 'synthesis', queryVariants: ['开始熟起来', '关系变熟'] })
+    expect(parseAiQueryUnderstanding(JSON.stringify({
+      mode: 'semantic', intent: 'general', semanticQuery: '关系变熟',
+      queryVariants: ['一', '二', '三', '四', '五'], confidence: 1, requiresClarification: false
+    }))).toBeNull()
+    expect(parseAiQueryUnderstanding(JSON.stringify({
+      mode: 'semantic', intent: 'general', semanticQuery: '关系变熟', sql: 'SELECT 1',
+      confidence: 1, requiresClarification: false
+    }))).toBeNull()
+  })
+
+  it.each([
+    'conversationId', 'wxid', 'messageId', 'sql', 'databasePath', 'filePath',
+    'startTime', 'endTime', 'evidenceId', 'tool', 'provider'
+  ])('%s is rejected as an unknown trusted field', (field) => {
+    expect(parseAiQueryUnderstanding(JSON.stringify({
+      intent: 'conversation_boundary', contactQuery: '张三', boundary: 'first',
+      confidence: 1, requiresClarification: false, [field]: 'unsafe'
+    }))).toBeNull()
+  })
+
+  it.each([
+    ['我和张三第一次聊天是什么时候？', '张三', 'first'],
+    ['我和【张三】第一次说话是什么时候？', '张三', 'first'],
+    ['我最早什么时候和张三聊过？', '张三', 'first'],
+    ['我和张三最后一次聊天是什么时候？', '张三', 'last'],
+    ['我最近一次和张三说话是什么时候？', '张三', 'last']
+  ])('%s -> %s boundary', (query, contactQuery, boundary) => {
+    expect(buildLocalAiSearchPlan(query)).toMatchObject({
+      intent: 'conversation_boundary', contactQuery, boundary
+    })
+  })
+
+  it('keeps ordinary conversation recall separate', () => {
+    expect(buildLocalAiSearchPlan('我和张三最近聊了什么')).toMatchObject({
+      intent: 'conversation_recall', contactQuery: '张三'
+    })
+  })
+
+  it.each([
+    '我和BOBO第一次讲话说的什么',
+    '我和BOBO第一次聊天说了什么，是什么时候',
+    '我最后一次跟BOBO说了什么'
+  ])('routes content boundary wording to suspicious fallback: %s', (query) => {
+    const plan = buildLocalAiSearchPlan(query)
+    expect(plan.intent).not.toBe('conversation_boundary')
+  })
+})
+
+describe('KnowledgeStore conversation boundary', () => {
+  it('uses the indexed time order and skips system messages', async () => {
+    const root = mkdtempSync(join(tmpdir(), 'trace-boundary-'))
+    roots.push(root)
+    const store = new KnowledgeStore(root, 'account', fts)
+    await store.index({
+      chunker: DEFAULT_KNOWLEDGE_CHUNKER,
+      conversations: [{
+        conversationId: 'conversation', completeSnapshot: true,
+        messages: [
+          { accountId: 'account', conversationId: 'conversation', messageId: 'system', createTime: 1, kind: 'system', text: '已添加联系人' },
+          { accountId: 'account', conversationId: 'conversation', messageId: 'first', createTime: 2, kind: 'text', text: '你好', senderName: '我' },
+          { accountId: 'account', conversationId: 'conversation', messageId: 'last', createTime: 3, kind: 'voice', voiceTranscript: '晚安', senderName: '张三' }
+        ]
+      }]
+    })
+    expect(store.search({ accountId: 'account', text: '', terms: [], limit: 1, conversationIds: ['conversation'], conversationBoundary: 'first' })[0]).toMatchObject({ messageId: 'first' })
+    expect(store.search({ accountId: 'account', text: '', terms: [], limit: 1, conversationIds: ['conversation'], conversationBoundary: 'last' })[0]).toMatchObject({ messageId: 'last', sourceKind: 'voice' })
+    store.close()
+  })
+})