test: 暂存代码

src/main/services/ai-search-evidence.ts

@@ -0,0 +1,217 @@
+import type {
+  AiSearchAggregation,
+  AiSearchFinalEvidence,
+  AiSearchPipelineEvidence
+} from '../../shared/ai-search'
+
+export type EvidenceBuildResult = {
+  evidence: AiSearchFinalEvidence[]
+  aggregation: AiSearchAggregation
+  candidateCount: number
+  deduplicatedCount: number
+  candidateRankingMs: number
+  evidenceBuildMs: number
+  aggregationMs: number
+}
+
+export type CitationValidationResult = {
+  answer: string
+  invalidCitationIds: string[]
+  status: 'valid' | 'sanitized'
+}
+
+export const evidenceIdentity = (
+  item: Pick<AiSearchPipelineEvidence, 'conversationId' | 'messageId'>
+): string => `${item.conversationId}\u0000${item.messageId}`
+
+const compareEvidence = (left: AiSearchPipelineEvidence, right: AiSearchPipelineEvidence): number =>
+  (left.score ?? 0) - (right.score ?? 0) ||
+  right.timestamp - left.timestamp ||
+  evidenceIdentity(left).localeCompare(evidenceIdentity(right))
+
+const personIdentity = (item: AiSearchFinalEvidence): string =>
+  item.senderId
+    ? `sender:${item.senderId}`
+    : `conversation:${item.conversationId}:name:${item.sender}`
+
+export function buildEvidenceAggregation(evidence: AiSearchFinalEvidence[]): AiSearchAggregation {
+  const people = new Map<
+    string,
+    {
+      id: string
+      name: string
+      messageCount: number
+      conversationIds: Set<string>
+      lastMessageAt: number
+      evidenceIds: AiSearchFinalEvidence['id'][]
+    }
+  >()
+  const conversations = new Map<
+    string,
+    {
+      id: string
+      name: string
+      type: 'user' | 'group'
+      messageCount: number
+      people: Set<string>
+      lastMessageAt: number
+      evidenceIds: AiSearchFinalEvidence['id'][]
+    }
+  >()
+
+  for (const item of evidence) {
+    const personId = personIdentity(item)
+    const person = people.get(personId) || {
+      id: personId,
+      name: item.sender,
+      messageCount: 0,
+      conversationIds: new Set<string>(),
+      lastMessageAt: item.timestamp,
+      evidenceIds: []
+    }
+    person.messageCount += 1
+    person.conversationIds.add(item.conversationId)
+    person.lastMessageAt = Math.max(person.lastMessageAt, item.timestamp)
+    person.evidenceIds.push(item.id)
+    people.set(personId, person)
+
+    const conversation = conversations.get(item.conversationId) || {
+      id: item.conversationId,
+      name: item.conversationName,
+      type: item.conversationType,
+      messageCount: 0,
+      people: new Set<string>(),
+      lastMessageAt: item.timestamp,
+      evidenceIds: []
+    }
+    conversation.messageCount += 1
+    conversation.people.add(personId)
+    conversation.lastMessageAt = Math.max(conversation.lastMessageAt, item.timestamp)
+    conversation.evidenceIds.push(item.id)
+    conversations.set(item.conversationId, conversation)
+  }
+
+  return {
+    messageCount: evidence.length,
+    peopleCount: people.size,
+    conversationCount: conversations.size,
+    people: Array.from(people.values())
+      .map((person) => ({
+        id: person.id,
+        name: person.name,
+        messageCount: person.messageCount,
+        conversationCount: person.conversationIds.size,
+        lastMessageAt: person.lastMessageAt,
+        evidenceIds: person.evidenceIds
+      }))
+      .sort(
+        (left, right) =>
+          right.messageCount - left.messageCount || right.lastMessageAt - left.lastMessageAt
+      ),
+    conversations: Array.from(conversations.values())
+      .map((conversation) => ({
+        id: conversation.id,
+        name: conversation.name,
+        type: conversation.type,
+        messageCount: conversation.messageCount,
+        peopleCount: conversation.people.size,
+        lastMessageAt: conversation.lastMessageAt,
+        evidenceIds: conversation.evidenceIds
+      }))
+      .sort(
+        (left, right) =>
+          right.messageCount - left.messageCount || right.lastMessageAt - left.lastMessageAt
+      )
+  }
+}
+
+/**
+ * Performs all candidate ordering, identity de-duplication, final limiting and
+ * program-owned citation assignment in one place. Nothing downstream receives
+ * the candidate list as an AI context.
+ */
+export function buildFinalEvidence(
+  candidates: AiSearchPipelineEvidence[],
+  limit: number,
+  options?: { strategy?: 'ranked' | 'conversation_coverage' }
+): EvidenceBuildResult {
+  const rankingStartedAt = Date.now()
+  const ranked = [...candidates].sort(compareEvidence)
+  const candidateRankingMs = Date.now() - rankingStartedAt
+
+  const evidenceStartedAt = Date.now()
+  const unique = new Map<string, AiSearchPipelineEvidence>()
+  for (const item of ranked) {
+    const identity = evidenceIdentity(item)
+    if (!unique.has(identity)) unique.set(identity, item)
+  }
+  const uniqueEvidence = Array.from(unique.values())
+  const selected =
+    options?.strategy === 'conversation_coverage'
+      ? selectConversationCoverage(uniqueEvidence, limit)
+      : uniqueEvidence.slice(0, Math.max(1, limit))
+  const evidence = selected.map((item, index) => ({ ...item, id: `E${index + 1}` as const }))
+  const evidenceBuildMs = Date.now() - evidenceStartedAt
+
+  const aggregationStartedAt = Date.now()
+  const aggregation = buildEvidenceAggregation(evidence)
+  const aggregationMs = Date.now() - aggregationStartedAt
+
+  return {
+    evidence,
+    aggregation,
+    candidateCount: candidates.length,
+    deduplicatedCount: unique.size,
+    candidateRankingMs,
+    evidenceBuildMs,
+    aggregationMs
+  }
+}
+
+/**
+ * A recent-conversation answer should cover separate local conversation chunks,
+ * not merely pick eight adjacent newest messages from one exchange.
+ */
+function selectConversationCoverage(
+  evidence: AiSearchPipelineEvidence[],
+  limit: number
+): AiSearchPipelineEvidence[] {
+  const max = Math.max(1, limit)
+  const byChunk = new Map<string, AiSearchPipelineEvidence[]>()
+  for (const item of evidence) {
+    const chunk = byChunk.get(item.chunkId) || []
+    chunk.push(item)
+    byChunk.set(item.chunkId, chunk)
+  }
+  const representatives = Array.from(byChunk.values())
+    .map((items) => [...items].sort(compareEvidence)[0])
+    .sort((left, right) => left.timestamp - right.timestamp)
+  if (representatives.length <= max) return representatives
+  const selected: AiSearchPipelineEvidence[] = []
+  for (let index = 0; index < max; index += 1) {
+    const position = Math.round((index * (representatives.length - 1)) / (max - 1 || 1))
+    const item = representatives[position]
+    if (item && !selected.includes(item)) selected.push(item)
+  }
+  return selected
+}
+
+/** Do not expose citations that cannot resolve to program-owned Final Evidence. */
+export function sanitizeAnswerCitations(
+  answer: string,
+  evidence: Array<Pick<AiSearchFinalEvidence, 'id'>>
+): CitationValidationResult {
+  const allowed = new Set(evidence.map((item) => item.id))
+  const invalidCitationIds = new Set<string>()
+  const sanitized = answer.replace(/\[E(\d+)\]/g, (citation, number: string) => {
+    const id = `E${number}`
+    if (allowed.has(id as AiSearchFinalEvidence['id'])) return citation
+    invalidCitationIds.add(id)
+    return ''
+  })
+  return {
+    answer: sanitized,
+    invalidCitationIds: Array.from(invalidCitationIds),
+    status: invalidCitationIds.size ? 'sanitized' : 'valid'
+  }
+}