mirror of
https://github.com/snapotter-hq/SnapOtter.git
synced 2026-08-03 07:46:42 +02:00
Security fixes:
- Add auth + ownership check to thumbnail endpoint (was unauthenticated)
- Validate ExifTool fieldsToRemove against safe tag name pattern
- Add SVG sanitization to pipeline execute and batch endpoints
- Replace basename() with sanitizeFilename() in 16 tool routes
- Escape SQL LIKE wildcards in file search to prevent pattern injection
- Improve settings HTML tag validation pattern
Bug fixes:
- Skip autoOrient for SVG inputs in pipeline (prevents misinterpretation)
- Remove double-encode in compress targetSize (was degrading quality)
- Fix bg-effects alpha value from 255 to 1.0 (Sharp expects float)
- Guard download stream error handler against headers-already-sent race
- Use O_EXCL atomic file creation for install lock (fixes TOCTOU race)
- Truncate collage file array to template image count
UX fixes:
- Accept empty JSON bodies on POST endpoints (install/uninstall)
- Custom JSON content type parser that treats empty body as {}
738 lines
25 KiB
TypeScript
738 lines
25 KiB
TypeScript
/**
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* Pipeline execution, save, list, and delete routes.
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*
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* POST /api/v1/pipeline/execute — Execute a pipeline (array of tool steps)
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* POST /api/v1/pipeline/save — Save a pipeline definition
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* GET /api/v1/pipeline/list — List saved pipelines
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* DELETE /api/v1/pipeline/:id — Delete a saved pipeline
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*/
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import { randomUUID } from "node:crypto";
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import { writeFile } from "node:fs/promises";
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import { join } from "node:path";
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import { ANALYTICS_EVENTS, getBundleForTool, TOOL_BUNDLE_MAP } from "@snapotter/shared";
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import archiver from "archiver";
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import { eq } from "drizzle-orm";
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import type { FastifyInstance, FastifyReply, FastifyRequest } from "fastify";
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import PQueue from "p-queue";
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import { z } from "zod";
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import { env } from "../config.js";
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import { db, schema } from "../db/index.js";
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import { trackEvent } from "../lib/analytics.js";
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import { autoOrient } from "../lib/auto-orient.js";
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import { resolveConcurrency } from "../lib/env.js";
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import { formatZodErrors } from "../lib/errors.js";
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import { isToolInstalled } from "../lib/feature-status.js";
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import { validateImageBuffer } from "../lib/file-validation.js";
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import { sanitizeFilename } from "../lib/filename.js";
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import { decodeToSharpCompat, needsCliDecode } from "../lib/format-decoders.js";
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import { decodeHeic } from "../lib/heic-converter.js";
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import { isSvgBuffer, sanitizeSvg } from "../lib/svg-sanitize.js";
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import { createWorkspace } from "../lib/workspace.js";
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import { hasEffectivePermission } from "../permissions.js";
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import { requireAuth } from "../plugins/auth.js";
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import { type JobProgress, updateJobProgress } from "./progress.js";
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import { getRegisteredToolIds, getToolConfig } from "./tool-factory.js";
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/** Schema for a single pipeline step. */
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const pipelineStepSchema = z.object({
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toolId: z.string(),
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settings: z.record(z.unknown()).default({}),
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});
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/** Schema for a full pipeline definition. */
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const stepsSchema =
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env.MAX_PIPELINE_STEPS > 0
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? z
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.array(pipelineStepSchema)
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.min(1, "Pipeline must have at least one step")
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.max(env.MAX_PIPELINE_STEPS, "Pipeline exceeds maximum steps")
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: z.array(pipelineStepSchema).min(1, "Pipeline must have at least one step");
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const pipelineDefinitionSchema = z.object({
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steps: stepsSchema,
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});
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/** Schema for saving a pipeline. */
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const savePipelineSchema = z.object({
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name: z.string().min(1, "Pipeline name is required").max(100),
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description: z.string().max(500).optional(),
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steps: stepsSchema,
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});
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export async function registerPipelineRoutes(app: FastifyInstance): Promise<void> {
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/**
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* POST /api/v1/pipeline/execute
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*
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* Accepts multipart with:
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* - A file part (the image to process)
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* - A "pipeline" field containing JSON: { steps: [{ toolId, settings }, ...] }
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*
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* Processes the image through each step sequentially.
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* The output of step N becomes the input of step N+1.
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* Returns the final processed image for download.
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*/
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app.post("/api/v1/pipeline/execute", async (request: FastifyRequest, reply: FastifyReply) => {
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let fileBuffer: Buffer | null = null;
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let filename = "image";
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let pipelineRaw: string | null = null;
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// Parse multipart
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try {
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const parts = request.parts();
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for await (const part of parts) {
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if (part.type === "file") {
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const chunks: Buffer[] = [];
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for await (const chunk of part.file) {
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chunks.push(chunk);
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}
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fileBuffer = Buffer.concat(chunks);
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filename = sanitizeFilename(part.filename ?? "image");
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} else if (part.fieldname === "pipeline") {
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pipelineRaw = part.value as string;
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}
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}
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} catch (err) {
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return reply.status(400).send({
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error: "Failed to parse multipart request",
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details: err instanceof Error ? err.message : String(err),
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});
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}
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if (!fileBuffer || fileBuffer.length === 0) {
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return reply.status(400).send({ error: "No image file provided" });
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}
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// Validate the initial image
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const validation = await validateImageBuffer(fileBuffer, filename);
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if (!validation.valid) {
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return reply.status(400).send({
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error: `Invalid image: ${validation.reason}`,
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});
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}
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// Decode HEIC/HEIF input via system heif-dec
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if (validation.format === "heif") {
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try {
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fileBuffer = await decodeHeic(fileBuffer);
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// Update filename extension to match the decoded format
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const ext = filename.match(/\.[^.]+$/)?.[0];
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if (ext) filename = `${filename.slice(0, -ext.length)}.png`;
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} catch (err) {
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return reply.status(422).send({
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error: "Failed to decode HEIC file. Ensure libheif-examples is installed.",
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details: err instanceof Error ? err.message : String(err),
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});
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}
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}
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// Decode CLI-decoded formats (RAW, TGA, PSD, EXR, HDR)
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if (needsCliDecode(validation.format)) {
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try {
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fileBuffer = await decodeToSharpCompat(fileBuffer, validation.format);
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const ext = filename.match(/\.[^.]+$/)?.[0];
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if (ext) filename = `${filename.slice(0, -ext.length)}.png`;
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} catch (err) {
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return reply.status(422).send({
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error: `Failed to decode ${validation.format} file`,
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details: err instanceof Error ? err.message : String(err),
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});
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}
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}
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// Sanitize SVG input and normalize EXIF orientation
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const isSvg = isSvgBuffer(fileBuffer);
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if (isSvg) {
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fileBuffer = sanitizeSvg(fileBuffer);
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} else {
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fileBuffer = await autoOrient(fileBuffer);
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}
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// Parse and validate the pipeline definition
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if (!pipelineRaw) {
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return reply.status(400).send({ error: "No pipeline definition provided" });
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}
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let pipeline: z.infer<typeof pipelineDefinitionSchema>;
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try {
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const parsed = JSON.parse(pipelineRaw);
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const result = pipelineDefinitionSchema.safeParse(parsed);
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if (!result.success) {
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return reply.status(400).send({
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error: "Invalid pipeline definition",
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details: formatZodErrors(result.error.issues),
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});
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}
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pipeline = result.data;
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} catch {
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return reply.status(400).send({ error: "Pipeline must be valid JSON" });
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}
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// Validate all tool IDs exist before starting
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for (let i = 0; i < pipeline.steps.length; i++) {
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const step = pipeline.steps[i];
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// Route content-aware resize to its dedicated tool
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const resolvedToolId =
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step.toolId === "resize" && step.settings?.contentAware
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? "content-aware-resize"
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: step.toolId;
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const toolConfig = getToolConfig(resolvedToolId);
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if (!toolConfig) {
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return reply.status(400).send({
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error: `Step ${i + 1} (${step.toolId}): Tool not found or not available`,
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});
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}
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// Guard: check if the tool's AI feature bundle is installed
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if (!isToolInstalled(resolvedToolId)) {
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const bundle = getBundleForTool(resolvedToolId);
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return reply.status(501).send({
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error: `Step ${i + 1} (${step.toolId}): Feature "${bundle?.name}" is not installed`,
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code: "FEATURE_NOT_INSTALLED",
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feature: TOOL_BUNDLE_MAP[resolvedToolId],
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featureName: bundle?.name ?? resolvedToolId,
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});
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}
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// Validate the settings for this tool
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const settingsResult = toolConfig.settingsSchema.safeParse(step.settings);
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if (!settingsResult.success) {
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return reply.status(400).send({
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error: `Step ${i + 1} (${step.toolId}): Invalid settings`,
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details: settingsResult.error.issues.map(
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(iss: { path: (string | number)[]; message: string }) => ({
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path: iss.path.join("."),
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message: iss.message,
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}),
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),
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});
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}
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}
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// Execute the pipeline: pass the buffer through each step sequentially
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const startTime = Date.now();
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let currentBuffer = fileBuffer;
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let currentFilename = filename;
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const stepResults: Array<{ step: number; toolId: string; size: number }> = [];
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try {
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for (let i = 0; i < pipeline.steps.length; i++) {
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const step = pipeline.steps[i];
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// Route content-aware resize to its dedicated tool
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const resolvedToolId =
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step.toolId === "resize" && step.settings?.contentAware
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? "content-aware-resize"
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: step.toolId;
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const toolConfig = getToolConfig(resolvedToolId);
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if (!toolConfig) {
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return reply.status(400).send({
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error: `Step ${i + 1} (${step.toolId}): Tool not found or not available`,
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});
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}
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try {
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const settings = toolConfig.settingsSchema.parse(step.settings);
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const result = await toolConfig.process(currentBuffer, settings, currentFilename);
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stepResults.push({
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step: i + 1,
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toolId: step.toolId,
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size: result.buffer.length,
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});
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currentBuffer = result.buffer;
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currentFilename = result.filename;
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} catch (stepErr) {
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const msg = stepErr instanceof Error ? stepErr.message : "Processing failed";
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throw new Error(`Step ${i + 1} (${step.toolId}): ${msg}`);
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}
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}
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} catch (err) {
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const message = err instanceof Error ? err.message : "Pipeline processing failed";
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trackEvent(request, ANALYTICS_EVENTS.PIPELINE_EXECUTED, {
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step_count: pipeline.steps.length,
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tool_ids: pipeline.steps.map((s: { toolId: string }) => s.toolId),
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is_batch: false,
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duration_ms: Date.now() - startTime,
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status: "failed",
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});
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return reply.status(422).send({
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error: message,
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completedSteps: stepResults,
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});
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}
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// Save the final output to workspace
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const jobId = randomUUID();
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const workspacePath = await createWorkspace(jobId);
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const outputPath = join(workspacePath, "output", currentFilename);
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await writeFile(outputPath, currentBuffer);
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// Also save the original input for reference
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const inputPath = join(workspacePath, "input", filename);
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await writeFile(inputPath, fileBuffer);
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trackEvent(request, ANALYTICS_EVENTS.PIPELINE_EXECUTED, {
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step_count: pipeline.steps.length,
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tool_ids: pipeline.steps.map((s: { toolId: string }) => s.toolId),
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is_batch: false,
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duration_ms: Date.now() - startTime,
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status: "completed",
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});
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return reply.send({
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jobId,
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downloadUrl: `/api/v1/download/${jobId}/${encodeURIComponent(currentFilename)}`,
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originalSize: fileBuffer.length,
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processedSize: currentBuffer.length,
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stepsCompleted: stepResults.length,
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steps: stepResults,
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});
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});
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/**
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* POST /api/v1/pipeline/save
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*
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* Save a named pipeline definition for later reuse.
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*/
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app.post("/api/v1/pipeline/save", async (request: FastifyRequest, reply: FastifyReply) => {
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const user = requireAuth(request, reply);
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if (!user) return;
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const body = request.body as unknown;
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const result = savePipelineSchema.safeParse(body);
|
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|
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if (!result.success) {
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return reply.status(400).send({
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error: "Invalid pipeline definition",
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details: result.error.issues.map((i) => ({
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path: i.path.join("."),
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message: i.message,
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})),
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});
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}
|
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const { name, description, steps } = result.data;
|
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|
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// Validate all tool IDs exist
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for (let i = 0; i < steps.length; i++) {
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const toolConfig = getToolConfig(steps[i].toolId);
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if (!toolConfig) {
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return reply.status(400).send({
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error: `Step ${i + 1}: Tool "${steps[i].toolId}" not found`,
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});
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}
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}
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const id = randomUUID();
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db.insert(schema.pipelines)
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.values({
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id,
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userId: user.id,
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name,
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description: description ?? null,
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steps: JSON.stringify(steps),
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})
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.run();
|
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|
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return reply.status(201).send({
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id,
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name,
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description: description ?? null,
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steps,
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createdAt: new Date().toISOString(),
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});
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});
|
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|
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/**
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* GET /api/v1/pipeline/list
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*
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* List all saved pipelines.
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*/
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app.get("/api/v1/pipeline/list", async (request: FastifyRequest, reply: FastifyReply) => {
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const user = requireAuth(request, reply);
|
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if (!user) return;
|
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|
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// Admins see all pipelines; regular users see their own + legacy (no owner)
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const allRows = db.select().from(schema.pipelines).all();
|
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const rows = hasEffectivePermission(user, "pipelines:all")
|
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? allRows
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: allRows.filter((row) => !row.userId || row.userId === user.id);
|
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|
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const pipelines = rows.map((row) => ({
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id: row.id,
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name: row.name,
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description: row.description,
|
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steps: JSON.parse(row.steps),
|
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createdAt: row.createdAt.toISOString(),
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}));
|
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|
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return reply.send({ pipelines });
|
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});
|
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|
|
/**
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* DELETE /api/v1/pipeline/:id
|
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*
|
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* Delete a saved pipeline by its ID.
|
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*/
|
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app.delete(
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"/api/v1/pipeline/:id",
|
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async (request: FastifyRequest<{ Params: { id: string } }>, reply: FastifyReply) => {
|
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const user = requireAuth(request, reply);
|
|
if (!user) return;
|
|
|
|
const { id } = request.params;
|
|
|
|
const existing = db.select().from(schema.pipelines).where(eq(schema.pipelines.id, id)).get();
|
|
|
|
if (!existing) {
|
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return reply.status(404).send({ error: "Pipeline not found" });
|
|
}
|
|
|
|
// Only the owner (or admin) can delete; legacy pipelines (no owner) can be deleted by anyone
|
|
if (
|
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existing.userId &&
|
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existing.userId !== user.id &&
|
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!hasEffectivePermission(user, "pipelines:all")
|
|
) {
|
|
return reply.status(403).send({ error: "Not authorized to delete this pipeline" });
|
|
}
|
|
|
|
db.delete(schema.pipelines).where(eq(schema.pipelines.id, id)).run();
|
|
|
|
return reply.send({ ok: true });
|
|
},
|
|
);
|
|
|
|
/**
|
|
* GET /api/v1/pipeline/tools
|
|
*
|
|
* Returns the IDs of tools that can be used as pipeline steps.
|
|
* Only tools registered via createToolRoute() support pipeline execution.
|
|
*/
|
|
app.get("/api/v1/pipeline/tools", async (_request: FastifyRequest, reply: FastifyReply) => {
|
|
return reply.send({ toolIds: getRegisteredToolIds() });
|
|
});
|
|
|
|
/**
|
|
* POST /api/v1/pipeline/batch
|
|
*
|
|
* Accepts multipart with multiple files + a "pipeline" JSON field.
|
|
* Runs the full pipeline on each file with concurrency control via p-queue.
|
|
* Returns a ZIP containing all processed results.
|
|
*/
|
|
app.post("/api/v1/pipeline/batch", async (request: FastifyRequest, reply: FastifyReply) => {
|
|
// ── Parse multipart ──────────────────────────────────────────────
|
|
interface ParsedFile {
|
|
buffer: Buffer;
|
|
filename: string;
|
|
}
|
|
|
|
const files: ParsedFile[] = [];
|
|
let pipelineRaw: string | null = null;
|
|
let clientJobId: string | null = null;
|
|
|
|
try {
|
|
const parts = request.parts();
|
|
for await (const part of parts) {
|
|
if (part.type === "file") {
|
|
const chunks: Buffer[] = [];
|
|
for await (const chunk of part.file) {
|
|
chunks.push(chunk);
|
|
}
|
|
const buffer = Buffer.concat(chunks);
|
|
if (buffer.length > 0) {
|
|
files.push({
|
|
buffer,
|
|
filename: sanitizeFilename(part.filename ?? "image"),
|
|
});
|
|
}
|
|
} else if (part.fieldname === "pipeline") {
|
|
pipelineRaw = part.value as string;
|
|
} else if (part.fieldname === "clientJobId") {
|
|
clientJobId = part.value as string;
|
|
}
|
|
}
|
|
} catch (err) {
|
|
return reply.status(400).send({
|
|
error: "Failed to parse multipart request",
|
|
details: err instanceof Error ? err.message : String(err),
|
|
});
|
|
}
|
|
|
|
if (files.length === 0) {
|
|
return reply.status(400).send({ error: "No image files provided" });
|
|
}
|
|
|
|
// Enforce batch size limit
|
|
if (env.MAX_BATCH_SIZE > 0 && files.length > env.MAX_BATCH_SIZE) {
|
|
return reply.status(400).send({
|
|
error: `Too many files. Maximum batch size is ${env.MAX_BATCH_SIZE}`,
|
|
});
|
|
}
|
|
|
|
// ── Parse and validate pipeline definition ───────────────────────
|
|
if (!pipelineRaw) {
|
|
return reply.status(400).send({ error: "No pipeline definition provided" });
|
|
}
|
|
|
|
let pipeline: z.infer<typeof pipelineDefinitionSchema>;
|
|
try {
|
|
const parsed = JSON.parse(pipelineRaw);
|
|
const result = pipelineDefinitionSchema.safeParse(parsed);
|
|
if (!result.success) {
|
|
return reply.status(400).send({
|
|
error: "Invalid pipeline definition",
|
|
details: formatZodErrors(result.error.issues),
|
|
});
|
|
}
|
|
pipeline = result.data;
|
|
} catch {
|
|
return reply.status(400).send({ error: "Pipeline must be valid JSON" });
|
|
}
|
|
|
|
// Validate all tool IDs exist and settings are valid before processing
|
|
for (let i = 0; i < pipeline.steps.length; i++) {
|
|
const step = pipeline.steps[i];
|
|
const toolConfig = getToolConfig(step.toolId);
|
|
if (!toolConfig) {
|
|
return reply.status(400).send({
|
|
error: `Step ${i + 1}: Tool "${step.toolId}" not found`,
|
|
});
|
|
}
|
|
|
|
// Guard: check if the tool's AI feature bundle is installed
|
|
if (!isToolInstalled(step.toolId)) {
|
|
const bundle = getBundleForTool(step.toolId);
|
|
return reply.status(501).send({
|
|
error: `Step ${i + 1} (${step.toolId}): Feature "${bundle?.name}" is not installed`,
|
|
code: "FEATURE_NOT_INSTALLED",
|
|
feature: TOOL_BUNDLE_MAP[step.toolId],
|
|
featureName: bundle?.name ?? step.toolId,
|
|
});
|
|
}
|
|
|
|
const settingsResult = toolConfig.settingsSchema.safeParse(step.settings);
|
|
if (!settingsResult.success) {
|
|
return reply.status(400).send({
|
|
error: `Step ${i + 1} (${step.toolId}): Invalid settings`,
|
|
details: settingsResult.error.issues.map(
|
|
(iss: { path: (string | number)[]; message: string }) => ({
|
|
path: iss.path.join("."),
|
|
message: iss.message,
|
|
}),
|
|
),
|
|
});
|
|
}
|
|
}
|
|
|
|
// ── Progress tracking ────────────────────────────────────────────
|
|
const batchStartTime = Date.now();
|
|
const jobId = clientJobId || randomUUID();
|
|
|
|
const progress: JobProgress = {
|
|
jobId,
|
|
status: "processing",
|
|
totalFiles: files.length,
|
|
completedFiles: 0,
|
|
failedFiles: 0,
|
|
errors: [],
|
|
};
|
|
updateJobProgress({ ...progress });
|
|
|
|
// ── Process files through the pipeline with concurrency control ──
|
|
const queue = new PQueue({ concurrency: resolveConcurrency(env) });
|
|
|
|
const results: ({ buffer: Buffer; filename: string } | null)[] = new Array(files.length).fill(
|
|
null,
|
|
);
|
|
|
|
try {
|
|
const tasks = files.map((file, index) =>
|
|
queue.add(async () => {
|
|
progress.currentFile = file.filename;
|
|
updateJobProgress({ ...progress });
|
|
|
|
// Validate the image
|
|
const validation = await validateImageBuffer(file.buffer, file.filename);
|
|
if (!validation.valid) {
|
|
progress.failedFiles++;
|
|
progress.errors.push({
|
|
filename: file.filename,
|
|
error: `Invalid image: ${validation.reason}`,
|
|
});
|
|
progress.completedFiles++;
|
|
updateJobProgress({ ...progress });
|
|
return;
|
|
}
|
|
|
|
try {
|
|
let currentBuffer = file.buffer;
|
|
let currentFilename = file.filename;
|
|
|
|
// Decode HEIC/HEIF if needed
|
|
if (validation.format === "heif") {
|
|
currentBuffer = await decodeHeic(currentBuffer);
|
|
const ext = currentFilename.match(/\.[^.]+$/)?.[0];
|
|
if (ext) currentFilename = `${currentFilename.slice(0, -ext.length)}.png`;
|
|
}
|
|
|
|
// Decode CLI-decoded formats (RAW, TGA, PSD, EXR, HDR)
|
|
if (needsCliDecode(validation.format)) {
|
|
currentBuffer = await decodeToSharpCompat(currentBuffer, validation.format);
|
|
const ext = currentFilename.match(/\.[^.]+$/)?.[0];
|
|
if (ext) currentFilename = `${currentFilename.slice(0, -ext.length)}.png`;
|
|
}
|
|
|
|
// Sanitize SVG or normalize EXIF orientation
|
|
if (isSvgBuffer(currentBuffer)) {
|
|
currentBuffer = sanitizeSvg(currentBuffer);
|
|
} else {
|
|
currentBuffer = await autoOrient(currentBuffer);
|
|
}
|
|
|
|
// Run through all pipeline steps sequentially
|
|
for (let i = 0; i < pipeline.steps.length; i++) {
|
|
const step = pipeline.steps[i];
|
|
|
|
// Route content-aware resize to its dedicated tool
|
|
const resolvedToolId =
|
|
step.toolId === "resize" && step.settings?.contentAware
|
|
? "content-aware-resize"
|
|
: step.toolId;
|
|
|
|
const toolConfig = getToolConfig(resolvedToolId);
|
|
if (!toolConfig) {
|
|
throw new Error(`Step ${i + 1} (${step.toolId}): Tool not found or not available`);
|
|
}
|
|
|
|
try {
|
|
const settings = toolConfig.settingsSchema.parse(step.settings);
|
|
const result = await toolConfig.process(currentBuffer, settings, currentFilename);
|
|
currentBuffer = result.buffer;
|
|
currentFilename = result.filename;
|
|
} catch (stepErr) {
|
|
const msg = stepErr instanceof Error ? stepErr.message : "Processing failed";
|
|
throw new Error(`Step ${i + 1} (${step.toolId}): ${msg}`);
|
|
}
|
|
}
|
|
|
|
results[index] = { buffer: currentBuffer, filename: currentFilename };
|
|
|
|
progress.completedFiles++;
|
|
updateJobProgress({ ...progress });
|
|
} catch (err) {
|
|
progress.failedFiles++;
|
|
progress.errors.push({
|
|
filename: file.filename,
|
|
error: err instanceof Error ? err.message : "Pipeline processing failed",
|
|
});
|
|
progress.completedFiles++;
|
|
updateJobProgress({ ...progress });
|
|
}
|
|
}),
|
|
);
|
|
|
|
await Promise.all(tasks);
|
|
} catch (err) {
|
|
request.log.error({ err }, "Unexpected error in pipeline batch queue");
|
|
}
|
|
|
|
// ── Finalize progress ────────────────────────────────────────────
|
|
progress.status = progress.failedFiles === progress.totalFiles ? "failed" : "completed";
|
|
progress.currentFile = undefined;
|
|
updateJobProgress({ ...progress });
|
|
|
|
// ── Deduplicate output filenames ─────────────────────────────────
|
|
const usedNames = new Set<string>();
|
|
function getUniqueName(name: string): string {
|
|
if (!usedNames.has(name)) {
|
|
usedNames.add(name);
|
|
return name;
|
|
}
|
|
const dotIdx = name.lastIndexOf(".");
|
|
const base = dotIdx > 0 ? name.slice(0, dotIdx) : name;
|
|
const ext = dotIdx > 0 ? name.slice(dotIdx) : "";
|
|
let counter = 1;
|
|
let candidate = `${base}_${counter}${ext}`;
|
|
while (usedNames.has(candidate)) {
|
|
counter++;
|
|
candidate = `${base}_${counter}${ext}`;
|
|
}
|
|
usedNames.add(candidate);
|
|
return candidate;
|
|
}
|
|
|
|
const fileResultsMap: Record<string, string> = {};
|
|
for (let i = 0; i < results.length; i++) {
|
|
const entry = results[i];
|
|
if (entry) {
|
|
const uniqueName = getUniqueName(entry.filename);
|
|
entry.filename = uniqueName;
|
|
fileResultsMap[String(i)] = uniqueName;
|
|
}
|
|
}
|
|
|
|
// If every file failed, return an error instead of an empty ZIP
|
|
if (progress.status === "failed") {
|
|
trackEvent(request, ANALYTICS_EVENTS.PIPELINE_EXECUTED, {
|
|
step_count: pipeline.steps.length,
|
|
tool_ids: pipeline.steps.map((s: { toolId: string }) => s.toolId),
|
|
is_batch: true,
|
|
file_count: files.length,
|
|
duration_ms: Date.now() - batchStartTime,
|
|
status: "failed",
|
|
});
|
|
return reply.status(422).send({
|
|
error: "All files failed processing",
|
|
errors: progress.errors,
|
|
});
|
|
}
|
|
|
|
trackEvent(request, ANALYTICS_EVENTS.PIPELINE_EXECUTED, {
|
|
step_count: pipeline.steps.length,
|
|
tool_ids: pipeline.steps.map((s: { toolId: string }) => s.toolId),
|
|
is_batch: true,
|
|
file_count: files.length,
|
|
duration_ms: Date.now() - batchStartTime,
|
|
status: "completed",
|
|
});
|
|
|
|
// ── Stream ZIP response ──────────────────────────────────────────
|
|
reply.hijack();
|
|
reply.raw.writeHead(200, {
|
|
"Content-Type": "application/zip",
|
|
"Content-Disposition": `attachment; filename="pipeline-batch-${jobId.slice(0, 8)}.zip"`,
|
|
"Transfer-Encoding": "chunked",
|
|
"X-Job-Id": jobId,
|
|
"X-File-Results": JSON.stringify(fileResultsMap),
|
|
});
|
|
|
|
const archive = archiver("zip", { zlib: { level: 5 } });
|
|
|
|
archive.on("error", (err) => {
|
|
request.log.error({ err }, "Archiver error during pipeline batch processing");
|
|
if (!reply.raw.writableEnded) {
|
|
reply.raw.end();
|
|
}
|
|
});
|
|
|
|
archive.pipe(reply.raw);
|
|
|
|
// Append results in original upload order
|
|
for (const result of results) {
|
|
if (result) {
|
|
archive.append(result.buffer, { name: result.filename });
|
|
}
|
|
}
|
|
|
|
await archive.finalize();
|
|
});
|
|
|
|
app.log.info("Pipeline routes registered");
|
|
}
|