mirror of
https://github.com/hicccc77/WeFlow.git
synced 2026-10-25 17:36:32 +08:00
755 lines
29 KiB
TypeScript
755 lines
29 KiB
TypeScript
import type {
|
||
AgentInvestigationPlan,
|
||
AgentInvestigationPlanStepStatus,
|
||
} from './agentRunStore'
|
||
|
||
// 正常运行不设累计 token 或总耗时边界。唯一的时间保护是针对单次模型/工具流的
|
||
// 无活动校验：只要工作持续产生事件，就可以继续运行。
|
||
export const AGENT_INACTIVITY_TIMEOUT_MS = 10 * 60_000
|
||
|
||
// 模型自行选择每次实际读取哪些媒体以及数量；这里只保留单次调用的技术安全上限。
|
||
export const AGENT_MEDIA_REVIEW_VOICE_BATCH_SIZE = 32
|
||
export const AGENT_MEDIA_REVIEW_IMAGE_BATCH_SIZE = 32
|
||
|
||
export function explicitAgentMediaReadRequirements(questionValue: unknown): {
|
||
voice: boolean
|
||
image: boolean
|
||
} {
|
||
const question = String(questionValue || '').toLocaleLowerCase()
|
||
const voiceNoun = '(?:语音|录音|voice|audio)'
|
||
const imageNoun = '(?:图片|照片|截图|image|photo)'
|
||
const voiceAction = '(?:转写|听取|收听|听一下|实际听|实际读取|transcrib|listen)'
|
||
const imageAction = '(?:查看|看一下|实际看|读取|检查|inspect|view|open)'
|
||
return {
|
||
voice: new RegExp(`${voiceAction}[\\s\\S]{0,32}${voiceNoun}|${voiceNoun}[\\s\\S]{0,32}${voiceAction}`, 'iu').test(question),
|
||
image: new RegExp(`${imageAction}[\\s\\S]{0,32}${imageNoun}|${imageNoun}[\\s\\S]{0,32}${imageAction}`, 'iu').test(question),
|
||
}
|
||
}
|
||
|
||
export type AgentScopedTemporalContext = {
|
||
runStartedAt: number
|
||
datasetLatestMessageAt?: number
|
||
targetFirstMessageAt?: number
|
||
targetLastMessageAt?: number
|
||
wholeDaysFromTargetLastMessageToRunStart?: number
|
||
wholeDaysFromTargetLastMessageToDatasetLatest?: number
|
||
}
|
||
|
||
function normalizeEpochSeconds(value: unknown): number {
|
||
const numeric = Number(value || 0)
|
||
if (!Number.isFinite(numeric) || numeric <= 0) return 0
|
||
return Math.floor(numeric > 10_000_000_000 ? numeric / 1_000 : numeric)
|
||
}
|
||
|
||
function wholeDaysBetween(from: number, to: number): number | undefined {
|
||
if (!from || !to || to < from) return undefined
|
||
return Math.floor((to - from) / (24 * 60 * 60))
|
||
}
|
||
|
||
export function buildAgentScopedTemporalContext(input: {
|
||
scopeKind: 'global' | 'session'
|
||
runStartedAt: number
|
||
datasetLatestMessageAt?: number
|
||
targetFirstMessageAt?: number
|
||
targetLastMessageAt?: number
|
||
}): AgentScopedTemporalContext | null {
|
||
if (input.scopeKind !== 'session') return null
|
||
const runStartedAt = normalizeEpochSeconds(input.runStartedAt)
|
||
const datasetLatestMessageAt = normalizeEpochSeconds(input.datasetLatestMessageAt)
|
||
const targetFirstMessageAt = normalizeEpochSeconds(input.targetFirstMessageAt)
|
||
const targetLastMessageAt = normalizeEpochSeconds(input.targetLastMessageAt)
|
||
return {
|
||
runStartedAt,
|
||
datasetLatestMessageAt: datasetLatestMessageAt || undefined,
|
||
targetFirstMessageAt: targetFirstMessageAt || undefined,
|
||
targetLastMessageAt: targetLastMessageAt || undefined,
|
||
wholeDaysFromTargetLastMessageToRunStart: wholeDaysBetween(targetLastMessageAt, runStartedAt),
|
||
wholeDaysFromTargetLastMessageToDatasetLatest: wholeDaysBetween(targetLastMessageAt, datasetLatestMessageAt),
|
||
}
|
||
}
|
||
|
||
function cleanText(value: unknown): string {
|
||
return String(value ?? '').replace(/\u0000/g, '').trim()
|
||
}
|
||
|
||
const INVESTIGATION_PLAN_STATUSES = new Set<AgentInvestigationPlanStepStatus>([
|
||
'pending',
|
||
'in_progress',
|
||
'completed',
|
||
'skipped',
|
||
])
|
||
|
||
export type AgentInvestigationPlanDraft = {
|
||
title: string
|
||
questionUnderstanding: string
|
||
answerRequirements?: string[]
|
||
uncertainties?: string[]
|
||
researchIntent?: {
|
||
compareSources?: boolean
|
||
traceChangesOverTime?: boolean
|
||
readCompleteEvents?: boolean
|
||
focusSources?: string[]
|
||
focusEvents?: Array<{
|
||
source: string
|
||
anchorAt: string
|
||
purpose?: string
|
||
}>
|
||
rationale?: string
|
||
}
|
||
steps: Array<{
|
||
id: string
|
||
title: string
|
||
purpose?: string
|
||
status?: AgentInvestigationPlanStepStatus
|
||
note?: string
|
||
}>
|
||
}
|
||
|
||
export type AgentInvestigationPlanUpdate = {
|
||
title?: string
|
||
questionUnderstanding?: string
|
||
answerRequirements?: string[]
|
||
uncertainties?: string[]
|
||
researchIntent?: AgentInvestigationPlanDraft['researchIntent']
|
||
researchMemo?: string
|
||
sourceMemos?: Array<{
|
||
sourceLabel: string
|
||
memo: string
|
||
nextReading?: string
|
||
}>
|
||
selectedPageIds?: string[]
|
||
stepUpdates?: Array<{
|
||
id: string
|
||
title?: string
|
||
purpose?: string
|
||
status?: AgentInvestigationPlanStepStatus
|
||
note?: string
|
||
}>
|
||
addSteps?: Array<{
|
||
id: string
|
||
title: string
|
||
purpose?: string
|
||
status?: AgentInvestigationPlanStepStatus
|
||
note?: string
|
||
}>
|
||
}
|
||
|
||
function cleanPlanList(values: unknown, limit: number, maxLength: number): string[] {
|
||
if (!Array.isArray(values)) return []
|
||
return Array.from(new Set(values.map((item) => cleanText(item).slice(0, maxLength)).filter(Boolean))).slice(0, limit)
|
||
}
|
||
|
||
function cleanResearchIntent(
|
||
value: AgentInvestigationPlanDraft['researchIntent'] | undefined,
|
||
fallback?: AgentInvestigationPlan['researchIntent'],
|
||
): AgentInvestigationPlan['researchIntent'] | undefined {
|
||
if (!value) return fallback ? { ...fallback } : undefined
|
||
const focusEvents = value.focusEvents === undefined
|
||
? (fallback?.focusEvents || []).map((event) => ({ ...event }))
|
||
: (Array.isArray(value.focusEvents) ? value.focusEvents : [])
|
||
.map((event) => ({
|
||
source: cleanText(event?.source).slice(0, 512),
|
||
anchorAt: cleanText(event?.anchorAt).slice(0, 32),
|
||
purpose: cleanText(event?.purpose).slice(0, 400) || undefined,
|
||
}))
|
||
.filter((event) => event.source && /^\d{4}-\d{2}-\d{2}(?:[ T]\d{2}:\d{2}(?::\d{2})?)?$/.test(event.anchorAt))
|
||
.filter((event, index, events) => events.findIndex((candidate) => (
|
||
candidate.source === event.source && candidate.anchorAt === event.anchorAt
|
||
)) === index)
|
||
.slice(0, 16)
|
||
return {
|
||
compareSources: value.compareSources === undefined
|
||
? Boolean(fallback?.compareSources)
|
||
: value.compareSources === true,
|
||
traceChangesOverTime: value.traceChangesOverTime === undefined
|
||
? Boolean(fallback?.traceChangesOverTime)
|
||
: value.traceChangesOverTime === true,
|
||
readCompleteEvents: value.readCompleteEvents === undefined
|
||
? Boolean(fallback?.readCompleteEvents)
|
||
: value.readCompleteEvents === true,
|
||
focusSources: value.focusSources === undefined
|
||
? [...(fallback?.focusSources || [])]
|
||
: cleanPlanList(value.focusSources, 12, 512),
|
||
focusEvents,
|
||
rationale: cleanText(value.rationale).slice(0, 500) || fallback?.rationale,
|
||
}
|
||
}
|
||
|
||
function cleanPlanStep(
|
||
step: AgentInvestigationPlanDraft['steps'][number],
|
||
): AgentInvestigationPlan['steps'][number] | null {
|
||
const id = cleanText(step?.id).slice(0, 80)
|
||
const title = cleanText(step?.title).slice(0, 200)
|
||
if (!id || !title) return null
|
||
const status = INVESTIGATION_PLAN_STATUSES.has(step.status as AgentInvestigationPlanStepStatus)
|
||
? step.status as AgentInvestigationPlanStepStatus
|
||
: 'pending'
|
||
return {
|
||
id,
|
||
title,
|
||
purpose: cleanText(step.purpose).slice(0, 400) || undefined,
|
||
status,
|
||
note: cleanText(step.note).slice(0, 500) || undefined,
|
||
}
|
||
}
|
||
|
||
export function createAgentInvestigationPlan(
|
||
input: AgentInvestigationPlanDraft,
|
||
updatedAt = Date.now(),
|
||
): AgentInvestigationPlan {
|
||
const seen = new Set<string>()
|
||
const steps = (input.steps || [])
|
||
.map((step) => cleanPlanStep(step))
|
||
.filter((step): step is AgentInvestigationPlan['steps'][number] => Boolean(step))
|
||
.filter((step) => {
|
||
if (seen.has(step.id)) return false
|
||
seen.add(step.id)
|
||
return true
|
||
})
|
||
.slice(0, 20)
|
||
return {
|
||
title: cleanText(input.title).slice(0, 120),
|
||
questionUnderstanding: cleanText(input.questionUnderstanding).slice(0, 1_000),
|
||
answerRequirements: cleanPlanList(input.answerRequirements, 16, 400),
|
||
uncertainties: cleanPlanList(input.uncertainties, 16, 400),
|
||
researchIntent: cleanResearchIntent(input.researchIntent),
|
||
steps,
|
||
updatedAt,
|
||
}
|
||
}
|
||
|
||
export function updateAgentInvestigationPlan(
|
||
current: AgentInvestigationPlan,
|
||
input: AgentInvestigationPlanUpdate,
|
||
updatedAt = Date.now(),
|
||
): AgentInvestigationPlan {
|
||
const steps = current.steps.map((step) => ({ ...step }))
|
||
const byId = new Map(steps.map((step) => [step.id, step]))
|
||
for (const patch of (input.stepUpdates || []).slice(0, 20)) {
|
||
const step = byId.get(cleanText(patch.id).slice(0, 80))
|
||
if (!step) continue
|
||
const title = cleanText(patch.title).slice(0, 200)
|
||
const purpose = cleanText(patch.purpose).slice(0, 400)
|
||
const note = cleanText(patch.note).slice(0, 500)
|
||
if (title) step.title = title
|
||
if (patch.purpose !== undefined) step.purpose = purpose || undefined
|
||
if (patch.note !== undefined) step.note = note || undefined
|
||
if (INVESTIGATION_PLAN_STATUSES.has(patch.status as AgentInvestigationPlanStepStatus)) {
|
||
step.status = patch.status as AgentInvestigationPlanStepStatus
|
||
}
|
||
}
|
||
for (const candidate of (input.addSteps || []).slice(0, 20)) {
|
||
if (steps.length >= 20) break
|
||
const step = cleanPlanStep(candidate)
|
||
if (!step || byId.has(step.id)) continue
|
||
steps.push(step)
|
||
byId.set(step.id, step)
|
||
}
|
||
const questionUnderstanding = cleanText(input.questionUnderstanding).slice(0, 1_000)
|
||
const title = cleanText(input.title).slice(0, 120)
|
||
const currentTitle = cleanText(current.title).slice(0, 120) || '调查计划'
|
||
return {
|
||
title: input.title === undefined ? currentTitle : title || currentTitle,
|
||
questionUnderstanding: input.questionUnderstanding === undefined
|
||
? current.questionUnderstanding
|
||
: questionUnderstanding || current.questionUnderstanding,
|
||
answerRequirements: input.answerRequirements === undefined
|
||
? [...current.answerRequirements]
|
||
: cleanPlanList(input.answerRequirements, 16, 400),
|
||
uncertainties: input.uncertainties === undefined
|
||
? [...current.uncertainties]
|
||
: cleanPlanList(input.uncertainties, 16, 400),
|
||
researchIntent: input.researchIntent === undefined
|
||
? current.researchIntent ? { ...current.researchIntent } : undefined
|
||
: cleanResearchIntent(input.researchIntent, current.researchIntent),
|
||
steps,
|
||
updatedAt,
|
||
}
|
||
}
|
||
|
||
export function summarizeAgentInvestigationPlan(plan?: AgentInvestigationPlan) {
|
||
if (!plan) return null
|
||
const count = (status: AgentInvestigationPlanStepStatus) => plan.steps.filter((step) => step.status === status).length
|
||
return {
|
||
total: plan.steps.length,
|
||
completed: count('completed'),
|
||
inProgress: count('in_progress'),
|
||
pending: count('pending'),
|
||
skipped: count('skipped'),
|
||
activeSteps: plan.steps
|
||
.filter((step) => step.status === 'in_progress')
|
||
.map((step) => ({ id: step.id, title: step.title })),
|
||
unresolvedUncertainties: plan.uncertainties,
|
||
}
|
||
}
|
||
|
||
export function openAgentInvestigationPlanItems(plan?: AgentInvestigationPlan): {
|
||
steps: Array<{ id: string; title: string; status: AgentInvestigationPlanStepStatus }>
|
||
uncertainties: string[]
|
||
} {
|
||
if (!plan) return { steps: [], uncertainties: [] }
|
||
return {
|
||
steps: plan.steps
|
||
.filter((step) => step.status === 'pending' || step.status === 'in_progress')
|
||
.map((step) => ({ id: step.id, title: step.title, status: step.status })),
|
||
uncertainties: [...plan.uncertainties],
|
||
}
|
||
}
|
||
|
||
export function settleAgentInvestigationPlanForDelivery(
|
||
plan: AgentInvestigationPlan | undefined,
|
||
options: { reachedBoundary: boolean; updatedAt?: number },
|
||
): AgentInvestigationPlan | undefined {
|
||
if (!plan) return undefined
|
||
return {
|
||
...plan,
|
||
steps: plan.steps.map((step) => (
|
||
step.status === 'pending' || step.status === 'in_progress'
|
||
? { ...step, status: 'skipped' as const }
|
||
: { ...step }
|
||
)),
|
||
updatedAt: options.updatedAt ?? Date.now(),
|
||
}
|
||
}
|
||
|
||
export function sanitizeAgentFinalAnswer(
|
||
value: string,
|
||
sessionDisplayNames: ReadonlyMap<string, string>,
|
||
): string {
|
||
let answer = String(value || '')
|
||
for (const [sessionId, displayName] of sessionDisplayNames) {
|
||
if (!sessionId) continue
|
||
answer = answer.split(sessionId).join(displayName || '该联系人')
|
||
}
|
||
return answer
|
||
.replace(/\bpage_[A-Za-z0-9_-]{6,}\b/g, '已核对原文')
|
||
.replace(/\b(?:voice|image)_[A-Za-z0-9_-]{4,}\b/g, '对应媒体')
|
||
.replace(/\b(?:accountId|gh)_[A-Za-z0-9_-]+\b/gi, '该联系人')
|
||
.replace(/\b(?:local[\s_*`-]*id)\s*(?:[=:：]|为)?\s*[*_`]*\d+[*_`]*/gi, '')
|
||
.replace(/\b(?:message[\s_*`-]*(?:key|ref)|page[\s_*`-]*hash|dataset[\s_*`-]*fingerprint|session[\s_*`-]*id|next[\s_*`-]*cursor)\s*(?:[=:：]|为)?\s*[*_`]*[^\s,，、。;；]+[*_`]*/gi, '')
|
||
.replace(/[ \t]+\n/g, '\n')
|
||
.replace(/ {2,}/g, ' ')
|
||
.trim()
|
||
}
|
||
|
||
/**
|
||
* Detects a draft that presents an unattempted voice transcription as if the
|
||
* assistant lacked the capability. The runtime uses this signal to transcribe
|
||
* the visible voice messages and ask the model to answer again.
|
||
*/
|
||
export function agentAnswerDefersAvailableVoiceTranscription(value: unknown): boolean {
|
||
const answer = String(value ?? '').replace(/\u0000/g, '').trim().replace(/\s+/g, ' ')
|
||
if (!/(?:语音|音频|voice|audio)/i.test(answer)) return false
|
||
return [
|
||
/(?:语音|音频).{0,60}(?:只能|只)(?:看|看到|知道).{0,24}(?:回话|回复|文字|消息|内容)/u,
|
||
/(?:语音|音频).{0,60}(?:尚未|还没|没有|没能|不能|无法|没法).{0,16}(?:转写|识别|听|读取|看到|知道|理解)/u,
|
||
/(?:尚未|还没|没有|没能|不能|无法|没法).{0,16}(?:转写|识别|听|读取|理解).{0,60}(?:语音|音频)/u,
|
||
/(?:voice|audio).{0,80}(?:not transcribed|not available|can't|cannot|unable|could not|haven't|have not)/i,
|
||
/(?:can't|cannot|unable|could not|haven't|have not).{0,80}(?:hear|transcribe|read|understand).{0,40}(?:voice|audio)/i,
|
||
].some((pattern) => pattern.test(answer))
|
||
}
|
||
|
||
/** Collects opaque voice references only from fields that can be shown to the model. */
|
||
export function collectAgentVoiceRefs(value: unknown, refs = new Set<string>(), depth = 0): Set<string> {
|
||
if (depth > 5 || value === null || value === undefined) return refs
|
||
if (typeof value === 'string') {
|
||
for (const match of value.matchAll(/(?<![A-Za-z0-9_-])voice_[A-Za-z0-9_-]{4,}(?![A-Za-z0-9_-])/g)) refs.add(match[0])
|
||
return refs
|
||
}
|
||
if (Array.isArray(value)) {
|
||
for (const item of value) collectAgentVoiceRefs(item, refs, depth + 1)
|
||
return refs
|
||
}
|
||
if (typeof value !== 'object') return refs
|
||
const record = value as Record<string, unknown>
|
||
for (const key of ['voiceRef', 'mediaRefs', 'pageText', 'pages', 'contexts', 'conversations', 'results']) {
|
||
collectAgentVoiceRefs(record[key], refs, depth + 1)
|
||
}
|
||
return refs
|
||
}
|
||
|
||
/** Collects opaque image references only from fields that can be shown to the model. */
|
||
export function collectAgentImageRefs(value: unknown, refs = new Set<string>(), depth = 0): Set<string> {
|
||
if (depth > 5 || value === null || value === undefined) return refs
|
||
if (typeof value === 'string') {
|
||
for (const match of value.matchAll(/(?<![A-Za-z0-9_-])image_[A-Za-z0-9_-]{4,}(?![A-Za-z0-9_-])/g)) refs.add(match[0])
|
||
return refs
|
||
}
|
||
if (Array.isArray(value)) {
|
||
for (const item of value) collectAgentImageRefs(item, refs, depth + 1)
|
||
return refs
|
||
}
|
||
if (typeof value !== 'object') return refs
|
||
const record = value as Record<string, unknown>
|
||
for (const key of ['imageRef', 'mediaRefs', 'pageText', 'pages', 'contexts', 'conversations', 'results']) {
|
||
collectAgentImageRefs(record[key], refs, depth + 1)
|
||
}
|
||
return refs
|
||
}
|
||
|
||
export type AgentMediaReviewDecision = {
|
||
mediaRef: string
|
||
action: 'inspect' | 'skip'
|
||
reason: string
|
||
}
|
||
|
||
export type AgentMediaReviewSelection = {
|
||
mediaRef: string
|
||
reason: string
|
||
}
|
||
|
||
/**
|
||
* A review call is an execution request, not a candidate ballot. Every selected
|
||
* ref must be known, unique, and accompanied by the model's contextual reason;
|
||
* omission is how the model decides not to read an item.
|
||
*/
|
||
export function validateAgentMediaReviewSelections(
|
||
availableRefs: string[],
|
||
selections: AgentMediaReviewSelection[],
|
||
maximumSelections: number,
|
||
): {
|
||
success: boolean
|
||
duplicateRefs: string[]
|
||
unknownRefs: string[]
|
||
invalidReasonRefs: string[]
|
||
tooMany: boolean
|
||
} {
|
||
const available = new Set(
|
||
availableRefs.map((value) => String(value || '').trim()).filter(Boolean),
|
||
)
|
||
const counts = new Map<string, number>()
|
||
const unknownRefs: string[] = []
|
||
const invalidReasonRefs: string[] = []
|
||
for (const selection of selections || []) {
|
||
const mediaRef = String(selection?.mediaRef || '').trim()
|
||
if (!mediaRef) continue
|
||
counts.set(mediaRef, (counts.get(mediaRef) || 0) + 1)
|
||
if (!available.has(mediaRef) && !unknownRefs.includes(mediaRef)) unknownRefs.push(mediaRef)
|
||
if (!String(selection?.reason || '').trim() && !invalidReasonRefs.includes(mediaRef)) {
|
||
invalidReasonRefs.push(mediaRef)
|
||
}
|
||
}
|
||
const duplicateRefs = Array.from(counts)
|
||
.filter(([, count]) => count > 1)
|
||
.map(([mediaRef]) => mediaRef)
|
||
const limit = Math.max(1, Math.floor(Number(maximumSelections) || 1))
|
||
return {
|
||
success: counts.size > 0
|
||
&& duplicateRefs.length === 0
|
||
&& unknownRefs.length === 0
|
||
&& invalidReasonRefs.length === 0
|
||
&& selections.length <= limit,
|
||
duplicateRefs,
|
||
unknownRefs,
|
||
invalidReasonRefs,
|
||
tooMany: selections.length > limit,
|
||
}
|
||
}
|
||
|
||
/**
|
||
* Validates semantic media decisions. Voice review normally requires the whole
|
||
* offered batch; adaptive image review may deliberately select a non-empty
|
||
* subset and leave the rest pending for a later batch.
|
||
*/
|
||
export function validateAgentMediaReviewDecisions(
|
||
expectedRefs: string[],
|
||
decisions: AgentMediaReviewDecision[],
|
||
options: { requireAll?: boolean } = {},
|
||
): {
|
||
success: boolean
|
||
missingRefs: string[]
|
||
duplicateRefs: string[]
|
||
unknownRefs: string[]
|
||
invalidReasonRefs: string[]
|
||
} {
|
||
const expected = Array.from(new Set(expectedRefs.map((value) => String(value || '').trim()).filter(Boolean)))
|
||
const expectedSet = new Set(expected)
|
||
const counts = new Map<string, number>()
|
||
const unknownRefs: string[] = []
|
||
const invalidReasonRefs: string[] = []
|
||
for (const decision of decisions || []) {
|
||
const mediaRef = String(decision?.mediaRef || '').trim()
|
||
if (!mediaRef) continue
|
||
counts.set(mediaRef, (counts.get(mediaRef) || 0) + 1)
|
||
if (!expectedSet.has(mediaRef) && !unknownRefs.includes(mediaRef)) unknownRefs.push(mediaRef)
|
||
if (!String(decision?.reason || '').trim() && !invalidReasonRefs.includes(mediaRef)) {
|
||
invalidReasonRefs.push(mediaRef)
|
||
}
|
||
}
|
||
const missingRefs = options.requireAll === false
|
||
? []
|
||
: expected.filter((mediaRef) => !counts.has(mediaRef))
|
||
const duplicateRefs = expected.filter((mediaRef) => (counts.get(mediaRef) || 0) > 1)
|
||
return {
|
||
success: missingRefs.length === 0
|
||
&& duplicateRefs.length === 0
|
||
&& unknownRefs.length === 0
|
||
&& invalidReasonRefs.length === 0
|
||
&& (options.requireAll !== false || counts.size > 0),
|
||
missingRefs,
|
||
duplicateRefs,
|
||
unknownRefs,
|
||
invalidReasonRefs,
|
||
}
|
||
}
|
||
|
||
/**
|
||
* Returns literal surrounding lines for media refs so the model can judge them
|
||
* in conversation context. Selection remains entirely with the model.
|
||
*/
|
||
export function buildAgentMediaReviewContexts(
|
||
pageTexts: string[],
|
||
mediaRefs: string[],
|
||
surroundingLines = 3,
|
||
): Array<{ mediaRef: string; context: string }> {
|
||
const pages = pageTexts.map((value) => String(value || '')).filter(Boolean)
|
||
const radius = Math.max(0, Math.min(8, Math.floor(Number(surroundingLines) || 0)))
|
||
return Array.from(new Set(mediaRefs.map((value) => String(value || '').trim()).filter(Boolean))).map((mediaRef) => {
|
||
for (const pageText of pages) {
|
||
const lines = pageText.split(/\r?\n/)
|
||
const index = lines.findIndex((line) => line.includes(mediaRef))
|
||
if (index < 0) continue
|
||
return {
|
||
mediaRef,
|
||
context: lines.slice(Math.max(0, index - radius), Math.min(lines.length, index + radius + 1)).join('\n'),
|
||
}
|
||
}
|
||
return { mediaRef, context: '' }
|
||
})
|
||
}
|
||
|
||
function collectAgentRawPageTexts(value: unknown, texts = new Set<string>(), depth = 0): Set<string> {
|
||
if (depth > 6 || value === null || value === undefined) return texts
|
||
if (Array.isArray(value)) {
|
||
for (const item of value) collectAgentRawPageTexts(item, texts, depth + 1)
|
||
return texts
|
||
}
|
||
if (typeof value !== 'object') return texts
|
||
const record = value as Record<string, unknown>
|
||
const pageText = String(record.pageText || '').trim()
|
||
if (pageText) texts.add(pageText)
|
||
for (const key of ['pages', 'contexts', 'conversations', 'results']) {
|
||
collectAgentRawPageTexts(record[key], texts, depth + 1)
|
||
}
|
||
return texts
|
||
}
|
||
|
||
/**
|
||
* Builds a literal, per-window reminder that media is available. It exposes
|
||
* every visible ref by type and a bounded set of surrounding contexts, but
|
||
* deliberately makes no importance decision for the model.
|
||
*/
|
||
export function buildAgentMediaAvailabilityReminder(
|
||
value: unknown,
|
||
contextLimitValue = 24,
|
||
): {
|
||
voiceCount: number
|
||
imageCount: number
|
||
voiceRefs: string[]
|
||
imageRefs: string[]
|
||
contextCandidates: Array<{ mediaRef: string; context: string }>
|
||
remainingContextCount: number
|
||
instruction: string
|
||
} | null {
|
||
const voiceRefs = Array.from(collectAgentVoiceRefs(value))
|
||
const imageRefs = Array.from(collectAgentImageRefs(value))
|
||
const mediaRefs = [...voiceRefs, ...imageRefs]
|
||
if (mediaRefs.length === 0) return null
|
||
const contextLimit = Math.max(1, Math.min(48, Math.floor(Number(contextLimitValue) || 24)))
|
||
const contextRefs = mediaRefs.slice(0, contextLimit)
|
||
const pageTexts = Array.from(collectAgentRawPageTexts(value))
|
||
return {
|
||
voiceCount: voiceRefs.length,
|
||
imageCount: imageRefs.length,
|
||
voiceRefs,
|
||
imageRefs,
|
||
contextCandidates: buildAgentMediaReviewContexts(pageTexts, contextRefs, 2),
|
||
remainingContextCount: Math.max(0, mediaRefs.length - contextRefs.length),
|
||
instruction: '本次原文窗口含可读取媒体：模型只把真正需要读取的 voiceRef 交给 review_focused_voice、把真正需要查看的 imageRef 交给 review_focused_images；工具参数中的每一项都会被实际读取，不支持在调用内部跳过。无需读取的项不要提交。本地窗口与 token 预算不代表媒体重要性。',
|
||
}
|
||
}
|
||
|
||
/** Raw timelines are model-selected longitudinal evidence, not reconnaissance. */
|
||
export function isAgentFocusedMediaReading(toolNameValue: unknown, readingPurposeValue?: unknown): boolean {
|
||
const toolName = String(toolNameValue || '').trim()
|
||
const readingPurpose = String(readingPurposeValue || '').trim()
|
||
if ([
|
||
'read_raw_timeline',
|
||
'read_message_thread',
|
||
'read_event_contexts',
|
||
'search_and_read_raw_messages',
|
||
'read_raw_message_ranges',
|
||
].includes(toolName)) return true
|
||
return toolName === 'read_raw_messages'
|
||
&& !['timeline', 'reconnaissance'].includes(readingPurpose)
|
||
}
|
||
|
||
export type AgentReadDepth = {
|
||
windows: number
|
||
tokenBudget: number
|
||
}
|
||
|
||
/**
|
||
* A previous sampled read covers a new one only when it is at least as wide and
|
||
* at least as deep. Comparing either dimension alone can incorrectly turn two
|
||
* shallow windows into a completed 24-window read (or vice versa).
|
||
*/
|
||
export function agentReadDepthCovers(completed: AgentReadDepth, requested: AgentReadDepth): boolean {
|
||
return completed.windows >= requested.windows
|
||
&& completed.tokenBudget >= requested.tokenBudget
|
||
}
|
||
|
||
/** Keep the non-dominated coverage points without inventing a depth never read. */
|
||
export function mergeAgentReadDepthCoverage(
|
||
existing: readonly AgentReadDepth[],
|
||
next: AgentReadDepth,
|
||
): AgentReadDepth[] {
|
||
if (existing.some((completed) => agentReadDepthCovers(completed, next))) return [...existing]
|
||
return [
|
||
...existing.filter((completed) => !agentReadDepthCovers(next, completed)),
|
||
next,
|
||
]
|
||
}
|
||
|
||
/** A date range is reusable as completed only when the result explicitly proves full coverage. */
|
||
export function isAgentExplicitRawRangeComplete(value: unknown): boolean {
|
||
if (!value || typeof value !== 'object' || Array.isArray(value)) return false
|
||
const record = value as Record<string, unknown>
|
||
return record.success === true
|
||
&& (record.requestSatisfied === true || record.coverageStatus === 'complete')
|
||
&& record.hasMore !== true
|
||
}
|
||
|
||
export function isLikelyIncompleteAgentAnswer(value: string): boolean {
|
||
const answer = String(value || '').trim()
|
||
if (!answer) return true
|
||
|
||
const fencedCodeBlocks = answer.match(/```/g)?.length || 0
|
||
if (fencedCodeBlocks % 2 !== 0) return true
|
||
|
||
const pairedMarkers: Array<[string, string]> = [
|
||
['**', '**'],
|
||
['__', '__'],
|
||
['“', '”'],
|
||
['「', '」'],
|
||
['『', '』'],
|
||
['（', '）'],
|
||
['(', ')'],
|
||
['【', '】'],
|
||
['[', ']'],
|
||
]
|
||
for (const [opening, closing] of pairedMarkers) {
|
||
const openingCount = answer.split(opening).length - 1
|
||
const closingCount = opening === closing
|
||
? openingCount
|
||
: answer.split(closing).length - 1
|
||
if (opening === closing ? openingCount % 2 !== 0 : openingCount > closingCount) return true
|
||
}
|
||
|
||
// 已闭合的代码块是有效的结束产物，即使最后一行代码没有正文标点。
|
||
if (fencedCodeBlocks > 0 && /```\s*$/.test(answer)) return false
|
||
|
||
const terminalText = answer
|
||
.replace(/(?:\*\*|__|~~)+\s*$/g, '')
|
||
.replace(/[”’"'）)\]】」』]+\s*$/g, '')
|
||
.trimEnd()
|
||
if (!terminalText) return true
|
||
if (/[。！？!?…\.](?:\s*)$/.test(terminalText)) return false
|
||
|
||
// 没有结束标记的最终正文含义不明；实践中，这是判断流式回答在输出或传输边界
|
||
// 被截断的最强供应商无关信号。重新合成比静默发布可能断裂的句子更安全。
|
||
return true
|
||
}
|
||
|
||
/**
|
||
* Decide whether an already generated answer needs another model request.
|
||
* A provider `stop` means the model deliberately completed the response; punctuation and
|
||
* Markdown-shape heuristics must not turn that successful answer into a mandatory rewrite.
|
||
* Those heuristics remain useful only when the provider cannot tell us how the stream ended.
|
||
*/
|
||
export function agentAnswerNeedsFinalSynthesis(value: string, finishReason: string): boolean {
|
||
const answer = String(value || '').trim()
|
||
if (!answer) return true
|
||
|
||
const normalizedFinishReason = String(finishReason || '').trim().toLowerCase()
|
||
if (normalizedFinishReason === 'stop') return false
|
||
if (['length', 'content-filter', 'error', 'tool-calls'].includes(normalizedFinishReason)) return true
|
||
return isLikelyIncompleteAgentAnswer(answer)
|
||
}
|
||
|
||
export function longestCompleteAgentAnswerPrefix(value: string, minimumLength = 320): string {
|
||
const answer = String(value || '').trim()
|
||
if (!answer) return ''
|
||
if (!isLikelyIncompleteAgentAnswer(answer)) return answer
|
||
|
||
const closeDanglingFormatting = (candidate: string): string => {
|
||
const stack: string[] = []
|
||
const symmetricMarkers = ['```', '**', '__']
|
||
const pairedMarkers = new Map<string, string>([
|
||
['“', '”'],
|
||
['「', '」'],
|
||
['『', '』'],
|
||
['（', '）'],
|
||
['(', ')'],
|
||
['【', '】'],
|
||
['[', ']'],
|
||
])
|
||
const closingMarkers = new Set(pairedMarkers.values())
|
||
for (let index = 0; index < candidate.length;) {
|
||
const symmetric = symmetricMarkers.find((marker) => candidate.startsWith(marker, index))
|
||
if (symmetric) {
|
||
if (stack.at(-1) === symmetric) stack.pop()
|
||
else stack.push(symmetric)
|
||
index += symmetric.length
|
||
continue
|
||
}
|
||
const character = candidate[index]
|
||
const expectedClosing = pairedMarkers.get(character)
|
||
if (expectedClosing) stack.push(expectedClosing)
|
||
else if (closingMarkers.has(character) && stack.at(-1) === character) stack.pop()
|
||
index += 1
|
||
}
|
||
return `${candidate}${stack.reverse().join('')}`
|
||
}
|
||
|
||
const terminalIndexes: number[] = []
|
||
for (let index = 0; index < answer.length; index += 1) {
|
||
if (/[。！？!?…\.]/.test(answer[index])) terminalIndexes.push(index)
|
||
}
|
||
for (let index = terminalIndexes.length - 1; index >= 0; index -= 1) {
|
||
const candidate = answer.slice(0, terminalIndexes[index] + 1).trim()
|
||
if (candidate.length < minimumLength) break
|
||
if (!isLikelyIncompleteAgentAnswer(candidate)) return candidate
|
||
const repairedCandidate = closeDanglingFormatting(candidate)
|
||
if (!isLikelyIncompleteAgentAnswer(repairedCandidate)) return repairedCandidate
|
||
}
|
||
return ''
|
||
}
|
||
|
||
export function sanitizeAgentSourceQuotes(value: string, sourceTexts: readonly string[]): string {
|
||
const normalizedSources = sourceTexts
|
||
.map((source) => String(source || '').replace(/\s+/g, ''))
|
||
.filter(Boolean)
|
||
if (normalizedSources.length === 0) return value
|
||
const appearsInSource = (quote: string) => normalizedSources.some((source) => source.includes(quote))
|
||
const sanitizeQuote = (captured: string) => {
|
||
const quote = String(captured || '').trim()
|
||
const normalized = quote.replace(/\s+/g, '')
|
||
if (appearsInSource(normalized)) return `“${quote}”`
|
||
const withoutEdgePunctuation = normalized.replace(/^[，,。！？!?；;：:]+|[，,。！？!?；;：:]+$/g, '')
|
||
if (withoutEdgePunctuation.length >= 2 && appearsInSource(withoutEdgePunctuation)) {
|
||
return `“${quote.replace(/^[，,。！？!?；;：:]+|[，,。！？!?；;：:]+$/g, '')}”`
|
||
}
|
||
return quote
|
||
}
|
||
// 移除不支持的引号前，先把相邻的带引号概念作为一对处理；否则
|
||
// “A”“B”会变成难以阅读的“AB”。
|
||
return String(value || '')
|
||
.replace(/[“"]([^”"\n]{2,160})[”"]\s*[“"]([^”"\n]{2,160})[”"]/g, (_full, left: string, right: string) => {
|
||
const sanitizedLeft = sanitizeQuote(left)
|
||
const sanitizedRight = sanitizeQuote(right)
|
||
return `${sanitizedLeft}和${sanitizedRight}`
|
||
})
|
||
.replace(/[“"]([^”"\n]{2,160})[”"]/g, (_full, captured: string) => sanitizeQuote(captured))
|
||
}
|