mirror of
https://github.com/codeflash-ai/codeflash-internal.git
synced 2026-05-04 18:25:18 +00:00
e5374c3f50
## Summary - Reverts lazy JWT_SECRET initialization — keeps eager fail-fast at module load - Adds `JWT_SECRET` secret to both `deploy_cfwebapp_to_azure.yml` and `nextjs-build.yaml` CI workflows so `next build` page data collection succeeds for the `/codeflash/auth/oauth/token` route ## Context The deploy workflow ([run #24425211765](https://github.com/codeflash-ai/codeflash-internal/actions/runs/24425211765/job/71357530269)) was failing because `JWT_SECRET` isn't available during CI build, causing an eager throw at module load time. The secret already exists as a GitHub repo secret.
175 lines
5.4 KiB
TypeScript
175 lines
5.4 KiB
TypeScript
import type { TimelineSection } from "./timeline-types"
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interface LLMExportInput {
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traceId: string
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functionName: string | null
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filePath: string | null
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originalCode: string | null
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sections: TimelineSection[]
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errors: Array<{
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error_type: string | null
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severity: string | null
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error_message: string | null
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context: {
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test_name?: string
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failure_reason?: string
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test_output?: string
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expected?: string
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actual?: string
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} | null
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created_at: Date
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}>
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totalDuration: number
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}
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function formatDuration(ms: number): string {
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if (ms < 1000) return `${Math.round(ms)}ms`
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return `${(ms / 1000).toFixed(1)}s`
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}
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function formatCost(cost: number | null | undefined): string {
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if (cost == null || cost === 0) return "$0.00"
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if (cost < 0.01) return `$${cost.toFixed(4)}`
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return `$${cost.toFixed(2)}`
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}
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export function formatTimelineForLLM(input: LLMExportInput): string {
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const { traceId, functionName, filePath, originalCode, sections, errors, totalDuration } = input
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const lines: string[] = []
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lines.push(`# Optimization Trace: ${traceId}`)
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if (functionName) lines.push(`## Function: ${functionName}`)
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if (filePath) lines.push(`File: ${filePath}`)
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lines.push("")
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if (originalCode) {
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lines.push("### Original Code")
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lines.push("```python")
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lines.push(originalCode)
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lines.push("```")
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lines.push("")
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}
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lines.push("---")
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lines.push("## Timeline")
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lines.push("")
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let sectionNum = 0
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for (const section of sections) {
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sectionNum++
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lines.push(`### ${sectionNum}. ${section.title}`)
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const meta: string[] = []
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if (section.model) meta.push(`Model: ${section.model}`)
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if (section.cost != null) meta.push(`Cost: ${formatCost(section.cost)}`)
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if (section.tokens != null) meta.push(`Tokens: ${section.tokens}`)
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if (section.duration != null) meta.push(`Duration: ${formatDuration(section.duration)}`)
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meta.push(`Status: ${section.status}`)
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if (meta.length > 0) lines.push(meta.join(" | "))
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lines.push("")
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const content = section.content
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if (content.type === "tests") {
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if (content.testFramework) {
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lines.push(`Test framework: ${content.testFramework}`)
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lines.push("")
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}
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for (const group of content.testGroups) {
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if (group.generated) {
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lines.push(`#### Generated Test ${group.index}`)
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lines.push("```python")
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lines.push(group.generated.code)
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lines.push("```")
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lines.push("")
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}
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}
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} else if (content.type === "candidate" || content.type === "refinement") {
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if (content.rank != null) {
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lines.push(`Rank: #${content.rank}${content.isBest ? " (Best)" : ""}`)
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lines.push("")
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}
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if (content.explanation) {
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lines.push("#### Explanation")
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lines.push(content.explanation)
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lines.push("")
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}
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lines.push("#### Optimized Code")
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lines.push("```python")
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lines.push(content.code)
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lines.push("```")
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lines.push("")
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} else if (content.type === "ranking") {
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if (content.explanation) {
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lines.push("#### Ranking Explanation")
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lines.push(content.explanation)
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lines.push("")
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}
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for (const r of content.rankings) {
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lines.push(`${r.rank}. ${r.label}${r.isBest ? " (Best)" : ""}`)
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}
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if (content.usedForPr) {
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lines.push("")
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lines.push("*Used for pull request*")
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}
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lines.push("")
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} else if (content.type === "summary") {
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lines.push(`- Total Cost: ${formatCost(content.metrics.totalCost)}`)
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lines.push(`- Total Tokens: ${content.metrics.totalTokens}`)
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lines.push(`- Total Duration: ${formatDuration(content.metrics.totalDuration)}`)
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lines.push(`- Candidates: ${content.metrics.candidatesCount}`)
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lines.push("")
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}
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if (section.debugData) {
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lines.push(`#### Debug: LLM Call ID: ${section.debugData.callId}`)
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lines.push("")
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}
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}
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if (errors.length > 0) {
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lines.push("---")
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lines.push("## Errors")
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lines.push("")
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for (const error of errors) {
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lines.push(
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`### ${error.error_type || "Error"}${error.severity ? ` (${error.severity})` : ""}`,
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)
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if (error.error_message) lines.push(error.error_message)
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if (error.context) {
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const ctx = error.context
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if (ctx.test_name) lines.push(`Test: ${ctx.test_name}`)
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if (ctx.failure_reason) lines.push(`Reason: ${ctx.failure_reason}`)
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if (ctx.test_output) {
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lines.push("```")
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lines.push(ctx.test_output)
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lines.push("```")
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}
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if (ctx.expected) lines.push(`Expected: ${ctx.expected}`)
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if (ctx.actual) lines.push(`Actual: ${ctx.actual}`)
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}
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lines.push("")
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}
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}
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const totalCost = sections.reduce((sum, s) => sum + (s.cost ?? 0), 0)
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const totalTokens = sections.reduce((sum, s) => sum + (s.tokens ?? 0), 0)
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const candidatesCount = sections.filter(
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s =>
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s.type === "optimization" ||
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s.type === "line_profiler" ||
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s.type === "refinement" ||
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s.type === "adaptive",
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).length
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lines.push("---")
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lines.push("## Summary")
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lines.push(`- Total Cost: ${formatCost(totalCost)}`)
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lines.push(`- Total Tokens: ${totalTokens}`)
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lines.push(`- Total Duration: ${formatDuration(totalDuration)}`)
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lines.push(`- Candidates: ${candidatesCount}`)
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if (errors.length > 0) lines.push(`- Errors: ${errors.length}`)
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lines.push("")
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return lines.join("\n")
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}
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