mirror of
https://github.com/DayuanJiang/next-ai-draw-io.git
synced 2026-09-01 17:10:24 +08:00
8fb9ef20bd
A reasoning model spends the output budget in order: thinking first, then prose, then the tool call. With 16000 the thinking alone can consume all of it, so the turn ends with finishReason "length" before display_diagram is ever called. The canvas stays empty and nothing surfaces in the UI, because no tool call means no tool error, and the client never reads finishReason. Measured on openrouter deepseek/deepseek-v4-flash, the model from the report: - max_tokens=800 with reasoning on returns reasoning_tokens=800, empty content, finish_reason length. So reasoning is billed against this budget, not exempt. - refining an existing diagram (19k chars of XML in the input) produced 49142 chars of reasoning, zero tool calls, finishReason "length" at 16000 - the same request at 40000 finished and called edit_diagram with 12 operations 64000 cannot just be sent to every model: bedrock claude-3-haiku caps at 4096, nova-lite at 10000, and the openrouter deepseek-r1 endpoint counts input and output against one 64000 ceiling. All three name the real limit in the 400, so parse it and retry once. Verified: nova-lite logs "64000 rejected, retrying with 10000" and then completes its tool call. Also expose the budget in Settings. It is sent as a header rather than read from env only, so desktop users can raise it themselves without an env file. vercel.json goes back to the 300s it had before #238 traded it for $2-4/month. That is now Vercel's own default, and billing pauses while the function waits on the model, so the saving that motivated 120s no longer applies. edgeone.json is left alone: its 120 may be that platform's actual ceiling.
878 lines
35 KiB
TypeScript
878 lines
35 KiB
TypeScript
import {
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APICallError,
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convertToModelMessages,
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createUIMessageStream,
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createUIMessageStreamResponse,
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InvalidToolInputError,
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LoadAPIKeyError,
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stepCountIs,
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streamText,
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} from "ai"
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import fs from "fs/promises"
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import { jsonrepair } from "jsonrepair"
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import path from "path"
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import { z } from "zod"
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import {
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getAIModel,
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SINGLE_SYSTEM_PROVIDERS,
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supportsPromptCaching,
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} from "@/lib/ai-providers"
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import { findCachedResponse } from "@/lib/cached-responses"
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import {
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isMinimalDiagram,
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replaceHistoricalToolInputs,
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validateFileParts,
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} from "@/lib/chat-helpers"
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import {
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checkAndIncrementRequest,
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isQuotaEnabled,
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recordTokenUsage,
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} from "@/lib/dynamo-quota-manager"
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import {
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getTelemetryConfig,
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setTraceInput,
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setTraceOutput,
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wrapWithObserve,
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} from "@/lib/langfuse"
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import {
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resolveMaxOutputTokens,
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withOutputTokenLimitFallback,
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} from "@/lib/output-token-limit"
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import { findServerModelById } from "@/lib/server-model-config"
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import { getSystemPrompt } from "@/lib/system-prompts"
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import { getUserIdFromRequest } from "@/lib/user-id"
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// No explicit cap: a reasoning model can spend minutes planning before it emits
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// the tool call, so take whatever the host allows. Vercel's own default is 300s,
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// which is also where Node's response-body timeout on the upstream stream lands.
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// Helper function to create cached stream response
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function createCachedStreamResponse(xml: string): Response {
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const toolCallId = `cached-${Date.now()}`
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const stream = createUIMessageStream({
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execute: async ({ writer }) => {
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writer.write({ type: "start" })
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writer.write({
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type: "tool-input-start",
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toolCallId,
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toolName: "display_diagram",
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})
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writer.write({
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type: "tool-input-delta",
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toolCallId,
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inputTextDelta: xml,
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})
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writer.write({
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type: "tool-input-available",
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toolCallId,
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toolName: "display_diagram",
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input: { xml },
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})
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writer.write({ type: "finish" })
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},
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})
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return createUIMessageStreamResponse({ stream })
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}
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// Inner handler function
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async function handleChatRequest(req: Request): Promise<Response> {
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// Check for access code
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const accessCodes =
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process.env.ACCESS_CODE_LIST?.split(",")
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.map((code) => code.trim())
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.filter(Boolean) || []
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if (accessCodes.length > 0) {
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const accessCodeHeader = req.headers.get("x-access-code")
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if (!accessCodeHeader || !accessCodes.includes(accessCodeHeader)) {
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return Response.json(
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{
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error: "Invalid or missing access code. Please configure it in Settings.",
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},
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{ status: 401 },
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)
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}
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}
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const body = await req.json()
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const { messages, xml, previousXml, sessionId } = body
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const customSystemMessage =
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typeof body.customSystemMessage === "string"
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? body.customSystemMessage.slice(0, 5000)
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: ""
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// Get user ID for Langfuse tracking and quota
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const userId = getUserIdFromRequest(req)
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// Validate sessionId for Langfuse (must be string, max 200 chars)
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const validSessionId =
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sessionId && typeof sessionId === "string" && sessionId.length <= 200
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? sessionId
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: undefined
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// Extract user input text for Langfuse trace
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// Find the last USER message, not just the last message (which could be assistant in multi-step tool flows)
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const lastUserMessage = [...messages]
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.reverse()
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.find((m: any) => m.role === "user")
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const userInputText =
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lastUserMessage?.parts?.find((p: any) => p.type === "text")?.text || ""
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// Update Langfuse trace with input, session, and user
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setTraceInput({
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input: userInputText,
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sessionId: validSessionId,
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userId: userId,
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})
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// === SERVER-SIDE QUOTA CHECK START ===
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// Quota is opt-in: only enabled when DYNAMODB_QUOTA_TABLE env var is set
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const hasOwnApiKey = !!(
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req.headers.get("x-ai-provider") &&
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(req.headers.get("x-ai-api-key") ||
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req.headers.get("x-aws-access-key-id") ||
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req.headers.get("x-vertex-api-key"))
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)
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// Skip quota check if: quota disabled, user has own API key, or is anonymous
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if (isQuotaEnabled() && !hasOwnApiKey && userId !== "anonymous") {
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const quotaCheck = await checkAndIncrementRequest(userId, {
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requests: Number(process.env.DAILY_REQUEST_LIMIT) || 10,
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tokens: Number(process.env.DAILY_TOKEN_LIMIT) || 200000,
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tpm: Number(process.env.TPM_LIMIT) || 20000,
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})
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if (!quotaCheck.allowed) {
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return Response.json(
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{
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error: quotaCheck.error,
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type: quotaCheck.type,
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used: quotaCheck.used,
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limit: quotaCheck.limit,
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},
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{ status: 429 },
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)
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}
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}
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// === SERVER-SIDE QUOTA CHECK END ===
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// === FILE VALIDATION START ===
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const fileValidation = validateFileParts(messages)
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if (!fileValidation.valid) {
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return Response.json({ error: fileValidation.error }, { status: 400 })
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}
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// === FILE VALIDATION END ===
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// === CACHE CHECK START ===
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const isFirstMessage = messages.length === 1
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const isEmptyDiagram = !xml || xml.trim() === "" || isMinimalDiagram(xml)
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if (isFirstMessage && isEmptyDiagram) {
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const lastMessage = messages[0]
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const textPart = lastMessage.parts?.find((p: any) => p.type === "text")
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const filePart = lastMessage.parts?.find((p: any) => p.type === "file")
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const cached = findCachedResponse(textPart?.text || "", !!filePart)
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if (cached) {
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return createCachedStreamResponse(cached.xml)
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}
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}
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// === CACHE CHECK END ===
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// Read client AI provider overrides from headers
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const provider = req.headers.get("x-ai-provider")
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let baseUrl = req.headers.get("x-ai-base-url")
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const selectedModelId = req.headers.get("x-selected-model-id")
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// For EdgeOne provider, construct full URL from request origin
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// because createOpenAI needs absolute URL, not relative path
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if (provider === "edgeone" && !baseUrl) {
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const origin = req.headers.get("origin") || new URL(req.url).origin
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baseUrl = `${origin}/api/edgeai`
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}
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// Get cookie header for EdgeOne authentication (eo_token, eo_time)
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const cookieHeader = req.headers.get("cookie")
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// Check if this is a server model with custom env var names
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let serverModelConfig: {
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apiKeyEnv?: string | string[]
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baseUrlEnv?: string
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provider?: string
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} = {}
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if (selectedModelId?.startsWith("server:")) {
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const serverModel = await findServerModelById(selectedModelId)
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console.log(
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`[Server Model Lookup] ID: ${selectedModelId}, Found: ${!!serverModel}, Provider: ${serverModel?.provider}`,
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)
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if (serverModel) {
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serverModelConfig = {
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apiKeyEnv: serverModel.apiKeyEnv,
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baseUrlEnv: serverModel.baseUrlEnv,
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// Use actual provider from config (client header may have incorrect value due to ID format change)
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provider: serverModel.provider,
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}
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}
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}
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const clientOverrides = {
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// Server model provider takes precedence over client header
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provider: serverModelConfig.provider || provider,
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baseUrl,
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apiKey: req.headers.get("x-ai-api-key"),
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modelId: req.headers.get("x-ai-model"),
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// AWS Bedrock credentials
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awsAccessKeyId: req.headers.get("x-aws-access-key-id"),
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awsSecretAccessKey: req.headers.get("x-aws-secret-access-key"),
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awsRegion: req.headers.get("x-aws-region"),
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awsSessionToken: req.headers.get("x-aws-session-token"),
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// Server model custom env var names
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...serverModelConfig,
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// Vertex AI credentials (Express Mode)
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vertexApiKey: req.headers.get("x-vertex-api-key"),
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// Pass cookies for EdgeOne Pages authentication
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...(provider === "edgeone" &&
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cookieHeader && {
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headers: { cookie: cookieHeader },
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}),
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}
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// Read minimal style preference from header
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const minimalStyle = req.headers.get("x-minimal-style") === "true"
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console.log(
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`[Client Overrides] provider: ${clientOverrides.provider}, modelId: ${clientOverrides.modelId}`,
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)
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// Get AI model with optional client overrides
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const {
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model: baseModel,
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providerOptions,
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headers,
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modelId,
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provider: resolvedProvider,
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} = getAIModel(clientOverrides)
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// Retry with a smaller budget if the provider rejects the requested one
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const model = withOutputTokenLimitFallback(baseModel)
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// User setting wins over server env, so desktop users can raise it themselves
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const maxOutputTokens = resolveMaxOutputTokens(
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req.headers.get("x-max-output-tokens"),
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)
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console.log(`[maxOutputTokens] ${maxOutputTokens}`)
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// Check if model supports prompt caching
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const shouldCache = supportsPromptCaching(modelId)
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console.log(
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`[Prompt Caching] ${shouldCache ? "ENABLED" : "DISABLED"} for model: ${modelId}`,
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)
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// Get the appropriate system prompt based on model (extended for Opus/Haiku 4.5)
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const systemMessage = getSystemPrompt(modelId, minimalStyle)
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const finalSystemMessage = customSystemMessage
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? `${systemMessage}\n\n## Custom Instructions\n${customSystemMessage}`
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: systemMessage
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// Extract file parts (images) from the last user message
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const fileParts =
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lastUserMessage?.parts?.filter((part: any) => part.type === "file") ||
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[]
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// Note: we used to pre-emptively reject images for models we guessed were
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// text-only (by name matching). That heuristic misfired on newer models
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// (see issue #874), so we now let the request through and surface the real
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// provider error if the model genuinely can't accept images.
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// User input only - XML is now in a separate cached system message
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const formattedUserInput = `User input:
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"""md
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${userInputText}
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"""`
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// Convert UIMessages to ModelMessages and add system message
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const modelMessages = await convertToModelMessages(messages)
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// DEBUG: Log incoming messages structure
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console.log("[route.ts] Incoming messages count:", messages.length)
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messages.forEach((msg: any, idx: number) => {
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console.log(
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`[route.ts] Message ${idx} role:`,
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msg.role,
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"parts count:",
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msg.parts?.length,
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)
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if (msg.parts) {
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msg.parts.forEach((part: any, partIdx: number) => {
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if (
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part.type === "tool-invocation" ||
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part.type === "tool-result"
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) {
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console.log(`[route.ts] Part ${partIdx}:`, {
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type: part.type,
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toolName: part.toolName,
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hasInput: !!part.input,
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inputType: typeof part.input,
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inputKeys:
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part.input && typeof part.input === "object"
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? Object.keys(part.input)
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: null,
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})
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}
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})
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}
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})
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// Replace historical tool call XML with placeholders to reduce tokens
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// Disabled by default - some models (e.g. minimax) copy placeholders instead of generating XML
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const enableHistoryReplace =
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process.env.ENABLE_HISTORY_XML_REPLACE === "true"
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const placeholderMessages = enableHistoryReplace
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? replaceHistoricalToolInputs(modelMessages)
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: modelMessages
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// Filter out messages with empty content arrays (Bedrock API rejects these)
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// This is a safety measure - ideally convertToModelMessages should handle all cases
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let enhancedMessages = placeholderMessages.filter(
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(msg: any) =>
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msg.content && Array.isArray(msg.content) && msg.content.length > 0,
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)
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// Filter out tool-calls with invalid inputs (from failed repair or interrupted streaming)
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// Bedrock API rejects messages where toolUse.input is not a valid JSON object
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enhancedMessages = enhancedMessages
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.map((msg: any) => {
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if (msg.role !== "assistant" || !Array.isArray(msg.content)) {
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return msg
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}
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const filteredContent = msg.content.filter((part: any) => {
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if (part.type === "tool-call") {
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// Check if input is a valid object (not null, undefined, or empty)
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if (
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!part.input ||
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typeof part.input !== "object" ||
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Object.keys(part.input).length === 0
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) {
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console.warn(
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`[route.ts] Filtering out tool-call with invalid input:`,
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{ toolName: part.toolName, input: part.input },
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)
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return false
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}
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}
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return true
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})
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return { ...msg, content: filteredContent }
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})
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.filter((msg: any) => msg.content && msg.content.length > 0)
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// DEBUG: Log modelMessages structure (what's being sent to AI)
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console.log("[route.ts] Model messages count:", enhancedMessages.length)
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enhancedMessages.forEach((msg: any, idx: number) => {
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console.log(
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`[route.ts] ModelMsg ${idx} role:`,
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msg.role,
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"content count:",
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msg.content?.length,
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)
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if (msg.content) {
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msg.content.forEach((part: any, partIdx: number) => {
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if (part.type === "tool-call" || part.type === "tool-result") {
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console.log(`[route.ts] Content ${partIdx}:`, {
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type: part.type,
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toolName: part.toolName,
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hasInput: !!part.input,
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inputType: typeof part.input,
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inputValue:
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part.input === undefined
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? "undefined"
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: part.input === null
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? "null"
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: "object",
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})
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}
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})
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}
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})
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// Update the last message with user input only (XML moved to separate cached system message)
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if (enhancedMessages.length >= 1) {
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const lastModelMessage = enhancedMessages[enhancedMessages.length - 1]
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if (lastModelMessage.role === "user") {
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// Build content array with user input text and file parts
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const contentParts: any[] = [
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{ type: "text", text: formattedUserInput },
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]
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// Add image parts back
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for (const filePart of fileParts) {
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contentParts.push({
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type: "image",
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image: filePart.url,
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mimeType: filePart.mediaType,
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})
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}
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enhancedMessages = [
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...enhancedMessages.slice(0, -1),
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{ ...lastModelMessage, content: contentParts },
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]
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}
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}
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// Add cache point to the last assistant message in conversation history
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// This caches the entire conversation prefix for subsequent requests
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// Strategy: system (cached) + history with last assistant (cached) + new user message
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if (shouldCache && enhancedMessages.length >= 2) {
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// Find the last assistant message (should be second-to-last, before current user message)
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for (let i = enhancedMessages.length - 2; i >= 0; i--) {
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if (enhancedMessages[i].role === "assistant") {
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enhancedMessages[i] = {
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...enhancedMessages[i],
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providerOptions: {
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bedrock: { cachePoint: { type: "default" } },
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},
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}
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break // Only cache the last assistant message
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}
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}
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}
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// System messages with multiple cache breakpoints for optimal caching:
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// - Breakpoint 1: System instructions + custom instructions - changes when user updates custom system message
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// - Breakpoint 2: Current XML context - changes per diagram, but constant within a conversation turn
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// Some providers (e.g. MiniMax) don't support multiple system messages
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// Merge them into a single system message for compatibility
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// Also merge for OpenAI-compatible providers with custom base URLs (e.g. vLLM, LMStudio)
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// because open-source model chat templates (Qwen, Llama, etc.) typically reject multiple system messages
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const isCustomOpenAIEndpoint =
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resolvedProvider === "openai" &&
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!!(
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baseUrl ||
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process.env.OPENAI_BASE_URL ||
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(serverModelConfig.baseUrlEnv &&
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process.env[serverModelConfig.baseUrlEnv])
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)
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const isSingleSystemProvider =
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SINGLE_SYSTEM_PROVIDERS.has(resolvedProvider) || isCustomOpenAIEndpoint
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const xmlContext = `${
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previousXml
|
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? `Previous diagram XML (before user's last message):
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|
"""xml
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${previousXml}
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"""
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|
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`
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: ""
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}Current diagram XML (AUTHORITATIVE - the source of truth):
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|
"""xml
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${xml || ""}
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"""
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|
|
IMPORTANT: The "Current diagram XML" is the SINGLE SOURCE OF TRUTH for what's on the canvas right now. The user can manually add, delete, or modify shapes directly in draw.io. Always count and describe elements based on the CURRENT XML, not on what you previously generated. If both previous and current XML are shown, compare them to understand what the user changed. When using edit_diagram, COPY search patterns exactly from the CURRENT XML - attribute order matters!`
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|
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const systemMessages = isSingleSystemProvider
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? [
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{
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role: "system" as const,
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content: `${finalSystemMessage}\n\n${xmlContext}`,
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},
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]
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: [
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// Cache breakpoint 1: Instructions (+ optional custom instructions)
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|
{
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role: "system" as const,
|
|
content: finalSystemMessage,
|
|
...(shouldCache && {
|
|
providerOptions: {
|
|
bedrock: { cachePoint: { type: "default" } },
|
|
},
|
|
}),
|
|
},
|
|
// Cache breakpoint 2: Previous and Current diagram XML context
|
|
{
|
|
role: "system" as const,
|
|
content: xmlContext,
|
|
...(shouldCache && {
|
|
providerOptions: {
|
|
bedrock: { cachePoint: { type: "default" } },
|
|
},
|
|
}),
|
|
},
|
|
]
|
|
|
|
const allMessages = [...systemMessages, ...enhancedMessages]
|
|
|
|
const result = streamText({
|
|
model,
|
|
abortSignal: req.signal,
|
|
// Must be sent: unset means the provider's own default, and Bedrock's is
|
|
// 4096, enough for a small diagram, so larger ones were cut off mid-attribute.
|
|
maxOutputTokens,
|
|
stopWhen: stepCountIs(5),
|
|
// Repair truncated tool calls when maxOutputTokens is reached mid-JSON
|
|
experimental_repairToolCall: async ({ toolCall, error }) => {
|
|
// DEBUG: Log what we're trying to repair
|
|
console.log(`[repairToolCall] Tool: ${toolCall.toolName}`)
|
|
console.log(
|
|
`[repairToolCall] Error: ${error.name} - ${error.message}`,
|
|
)
|
|
console.log(`[repairToolCall] Input type: ${typeof toolCall.input}`)
|
|
console.log(`[repairToolCall] Input value:`, toolCall.input)
|
|
|
|
// Only attempt repair for invalid tool input (broken JSON from truncation)
|
|
if (
|
|
error instanceof InvalidToolInputError ||
|
|
error.name === "AI_InvalidToolInputError"
|
|
) {
|
|
try {
|
|
// Pre-process to fix common LLM JSON errors that jsonrepair can't handle
|
|
let inputToRepair = toolCall.input
|
|
if (typeof inputToRepair === "string") {
|
|
// Fix `:=` instead of `: ` (LLM sometimes generates this)
|
|
inputToRepair = inputToRepair.replace(/:=/g, ": ")
|
|
// Fix `= "` instead of `: "`
|
|
inputToRepair = inputToRepair.replace(/=\s*"/g, ': "')
|
|
// Fix inconsistent quote escaping in XML attributes within JSON strings
|
|
// Pattern: attribute="value\" where opening quote is unescaped but closing is escaped
|
|
// Example: y="-20\" should be y=\"-20\"
|
|
inputToRepair = inputToRepair.replace(
|
|
/(\w+)="([^"]*?)\\"/g,
|
|
'$1=\\"$2\\"',
|
|
)
|
|
}
|
|
// Use jsonrepair to fix truncated JSON
|
|
const repairedInput = jsonrepair(inputToRepair)
|
|
console.log(
|
|
`[repairToolCall] Repaired truncated JSON for tool: ${toolCall.toolName}`,
|
|
)
|
|
return { ...toolCall, input: repairedInput }
|
|
} catch (repairError) {
|
|
console.warn(
|
|
`[repairToolCall] Failed to repair JSON for tool: ${toolCall.toolName}`,
|
|
repairError,
|
|
)
|
|
// Return a placeholder input to avoid API errors in multi-step
|
|
// The tool will fail gracefully on client side
|
|
if (toolCall.toolName === "edit_diagram") {
|
|
return {
|
|
...toolCall,
|
|
input: {
|
|
operations: [],
|
|
_error: "JSON repair failed - no operations to apply",
|
|
},
|
|
}
|
|
}
|
|
if (toolCall.toolName === "display_diagram") {
|
|
return {
|
|
...toolCall,
|
|
input: {
|
|
xml: "",
|
|
_error: "JSON repair failed - empty diagram",
|
|
},
|
|
}
|
|
}
|
|
return null
|
|
}
|
|
}
|
|
// Don't attempt to repair other errors (like NoSuchToolError)
|
|
return null
|
|
},
|
|
messages: allMessages,
|
|
...(providerOptions && { providerOptions }), // This now includes all reasoning configs
|
|
...(headers && { headers }),
|
|
// Langfuse telemetry config (returns undefined if not configured)
|
|
...(getTelemetryConfig({ sessionId: validSessionId, userId }) && {
|
|
experimental_telemetry: getTelemetryConfig({
|
|
sessionId: validSessionId,
|
|
userId,
|
|
}),
|
|
}),
|
|
onFinish: ({ text, totalUsage }) => {
|
|
// AI SDK 6 telemetry auto-reports token usage on its spans
|
|
setTraceOutput(text)
|
|
|
|
// Record token usage for server-side quota tracking (if enabled)
|
|
// Use totalUsage (cumulative across all steps) instead of usage (final step only)
|
|
// Include all 4 token types: input, output, cache read, cache write
|
|
if (
|
|
isQuotaEnabled() &&
|
|
!hasOwnApiKey &&
|
|
userId !== "anonymous" &&
|
|
totalUsage
|
|
) {
|
|
const totalTokens =
|
|
(totalUsage.inputTokens || 0) +
|
|
(totalUsage.outputTokens || 0) +
|
|
(totalUsage.cachedInputTokens || 0) +
|
|
(totalUsage.inputTokenDetails?.cacheWriteTokens || 0)
|
|
recordTokenUsage(userId, totalTokens)
|
|
}
|
|
},
|
|
tools: {
|
|
// Client-side tool that will be executed on the client
|
|
display_diagram: {
|
|
description: `Display a diagram on draw.io. Pass ONLY the mxCell elements - wrapper tags and root cells are added automatically.
|
|
|
|
VALIDATION RULES (XML will be rejected if violated):
|
|
1. Generate ONLY mxCell elements - NO wrapper tags (<mxfile>, <mxGraphModel>, <root>)
|
|
2. Do NOT include root cells (id="0" or id="1") - they are added automatically
|
|
3. All mxCell elements must be siblings - never nested
|
|
4. Every mxCell needs a unique id (start from "2")
|
|
5. Every mxCell needs a valid parent attribute (use "1" for top-level)
|
|
6. Escape special chars in values: < > & "
|
|
|
|
Example (generate ONLY this - no wrapper tags):
|
|
<mxCell id="lane1" value="Frontend" style="swimlane;" vertex="1" parent="1">
|
|
<mxGeometry x="40" y="40" width="200" height="200" as="geometry"/>
|
|
</mxCell>
|
|
<mxCell id="step1" value="Step 1" style="rounded=1;" vertex="1" parent="lane1">
|
|
<mxGeometry x="20" y="60" width="160" height="40" as="geometry"/>
|
|
</mxCell>
|
|
<mxCell id="lane2" value="Backend" style="swimlane;" vertex="1" parent="1">
|
|
<mxGeometry x="280" y="40" width="200" height="200" as="geometry"/>
|
|
</mxCell>
|
|
<mxCell id="step2" value="Step 2" style="rounded=1;" vertex="1" parent="lane2">
|
|
<mxGeometry x="20" y="60" width="160" height="40" as="geometry"/>
|
|
</mxCell>
|
|
<mxCell id="edge1" style="edgeStyle=orthogonalEdgeStyle;endArrow=classic;" edge="1" parent="1" source="step1" target="step2">
|
|
<mxGeometry relative="1" as="geometry"/>
|
|
</mxCell>
|
|
|
|
Notes:
|
|
- For AWS diagrams, use **AWS 2025 icons**.
|
|
- For animated connectors, add "flowAnimation=1" to edge style.
|
|
`,
|
|
inputSchema: z.object({
|
|
xml: z
|
|
.string()
|
|
.describe("XML string to be displayed on draw.io"),
|
|
}),
|
|
},
|
|
edit_diagram: {
|
|
description: `Edit the current diagram by ID-based operations (update/add/delete cells).
|
|
|
|
Operations:
|
|
- update: Replace an existing cell by its id. Provide cell_id and complete new_xml.
|
|
- add: Add a new cell. Provide cell_id (new unique id) and new_xml.
|
|
- delete: Remove a cell. Cascade is automatic: children AND edges (source/target) are auto-deleted. Only specify ONE cell_id.
|
|
|
|
For update/add, new_xml must be a complete mxCell element including mxGeometry.
|
|
|
|
⚠️ JSON ESCAPING: Every " inside new_xml MUST be escaped as \\". Example: id=\\"5\\" value=\\"Label\\"
|
|
|
|
Example - Add a rectangle:
|
|
{"operations": [{"operation": "add", "cell_id": "rect-1", "new_xml": "<mxCell id=\\"rect-1\\" value=\\"Hello\\" style=\\"rounded=0;\\" vertex=\\"1\\" parent=\\"1\\"><mxGeometry x=\\"100\\" y=\\"100\\" width=\\"120\\" height=\\"60\\" as=\\"geometry\\"/></mxCell>"}]}
|
|
|
|
Example - Delete container (children & edges auto-deleted):
|
|
{"operations": [{"operation": "delete", "cell_id": "2"}]}`,
|
|
inputSchema: z.object({
|
|
operations: z
|
|
.array(
|
|
z.object({
|
|
operation: z
|
|
.enum(["update", "add", "delete"])
|
|
.describe(
|
|
"Operation to perform: add, update, or delete",
|
|
),
|
|
cell_id: z
|
|
.string()
|
|
.describe(
|
|
"The id of the mxCell. Must match the id attribute in new_xml.",
|
|
),
|
|
new_xml: z
|
|
.string()
|
|
.optional()
|
|
.describe(
|
|
"Complete mxCell XML element (required for update/add)",
|
|
),
|
|
}),
|
|
)
|
|
.describe("Array of operations to apply"),
|
|
}),
|
|
},
|
|
append_diagram: {
|
|
description: `Continue generating diagram XML when previous display_diagram output was truncated due to length limits.
|
|
|
|
WHEN TO USE: Only call this tool after display_diagram was truncated (you'll see an error message about truncation).
|
|
|
|
CRITICAL INSTRUCTIONS:
|
|
1. Do NOT include any wrapper tags - just continue the mxCell elements
|
|
2. Continue from EXACTLY where your previous output stopped
|
|
3. Complete the remaining mxCell elements
|
|
4. If still truncated, call append_diagram again with the next fragment
|
|
|
|
Example: If previous output ended with '<mxCell id="x" style="rounded=1', continue with ';" vertex="1">...' and complete the remaining elements.`,
|
|
inputSchema: z.object({
|
|
xml: z
|
|
.string()
|
|
.describe(
|
|
"Continuation XML fragment to append (NO wrapper tags)",
|
|
),
|
|
}),
|
|
},
|
|
get_shape_library: {
|
|
description: `Get draw.io shape/icon library documentation with style syntax and shape names.
|
|
|
|
Available libraries:
|
|
- Cloud: aws4, azure2, gcp2, alibaba_cloud, openstack, salesforce
|
|
- Networking: cisco19, network, kubernetes, vvd, rack
|
|
- Business: bpmn, lean_mapping
|
|
- General: flowchart, basic, arrows2, infographic, sitemap
|
|
- UI/Mockups: android, material_design
|
|
- Enterprise: citrix, sap, mscae, atlassian
|
|
- Engineering: fluidpower, electrical, pid, cabinets, floorplan
|
|
- Icons: webicons
|
|
|
|
Call this tool to get shape names and usage syntax for a specific library.`,
|
|
inputSchema: z.object({
|
|
library: z
|
|
.string()
|
|
.describe(
|
|
"Library name (e.g., 'aws4', 'kubernetes', 'flowchart')",
|
|
),
|
|
}),
|
|
execute: async ({ library }) => {
|
|
// Sanitize input - prevent path traversal attacks
|
|
const sanitizedLibrary = library
|
|
.toLowerCase()
|
|
.replace(/[^a-z0-9_-]/g, "")
|
|
|
|
if (sanitizedLibrary !== library.toLowerCase()) {
|
|
return `Invalid library name "${library}". Use only letters, numbers, underscores, and hyphens.`
|
|
}
|
|
|
|
const baseDir = path.join(
|
|
process.cwd(),
|
|
"docs/shape-libraries",
|
|
)
|
|
const filePath = path.join(
|
|
baseDir,
|
|
`${sanitizedLibrary}.md`,
|
|
)
|
|
|
|
// Verify path stays within expected directory
|
|
const resolvedPath = path.resolve(filePath)
|
|
if (!resolvedPath.startsWith(path.resolve(baseDir))) {
|
|
return `Invalid library path.`
|
|
}
|
|
|
|
try {
|
|
const content = await fs.readFile(filePath, "utf-8")
|
|
return content
|
|
} catch (error) {
|
|
if (
|
|
(error as NodeJS.ErrnoException).code === "ENOENT"
|
|
) {
|
|
return `Library "${library}" not found. Available: aws4, azure2, gcp2, alibaba_cloud, cisco19, kubernetes, network, bpmn, flowchart, basic, arrows2, vvd, salesforce, citrix, sap, mscae, atlassian, fluidpower, electrical, pid, cabinets, floorplan, webicons, infographic, sitemap, android, material_design, lean_mapping, openstack, rack`
|
|
}
|
|
console.error(
|
|
`[get_shape_library] Error loading "${library}":`,
|
|
error,
|
|
)
|
|
return `Error loading library "${library}". Please try again.`
|
|
}
|
|
},
|
|
},
|
|
},
|
|
...(process.env.TEMPERATURE !== undefined && {
|
|
temperature: parseFloat(process.env.TEMPERATURE),
|
|
}),
|
|
})
|
|
|
|
return result.toUIMessageStreamResponse({
|
|
sendReasoning: true,
|
|
messageMetadata: ({ part }) => {
|
|
if (part.type === "finish") {
|
|
const usage = (part as any).totalUsage
|
|
// AI SDK 6 provides totalTokens directly
|
|
return {
|
|
totalTokens: usage?.totalTokens ?? 0,
|
|
finishReason: (part as any).finishReason,
|
|
}
|
|
}
|
|
return undefined
|
|
},
|
|
})
|
|
}
|
|
|
|
// Helper to categorize errors and return appropriate response
|
|
function handleError(error: unknown): Response {
|
|
console.error("Error in chat route:", error)
|
|
|
|
const isDev = process.env.NODE_ENV === "development"
|
|
|
|
// Check for specific AI SDK error types
|
|
if (APICallError.isInstance(error)) {
|
|
return Response.json(
|
|
{
|
|
error: error.message,
|
|
...(isDev && {
|
|
details: error.responseBody,
|
|
stack: error.stack,
|
|
}),
|
|
},
|
|
{ status: error.statusCode || 500 },
|
|
)
|
|
}
|
|
|
|
if (LoadAPIKeyError.isInstance(error)) {
|
|
return Response.json(
|
|
{
|
|
error: "Authentication failed. Please check your API key.",
|
|
...(isDev && {
|
|
stack: error.stack,
|
|
}),
|
|
},
|
|
{ status: 401 },
|
|
)
|
|
}
|
|
|
|
// Fallback for other errors with safety filter
|
|
const message =
|
|
error instanceof Error ? error.message : "An unexpected error occurred"
|
|
const status = (error as any)?.statusCode || (error as any)?.status || 500
|
|
|
|
// Prevent leaking API keys, tokens, or other sensitive data
|
|
const lowerMessage = message.toLowerCase()
|
|
const safeMessage =
|
|
lowerMessage.includes("key") ||
|
|
lowerMessage.includes("token") ||
|
|
lowerMessage.includes("sig") ||
|
|
lowerMessage.includes("signature") ||
|
|
lowerMessage.includes("secret") ||
|
|
lowerMessage.includes("password") ||
|
|
lowerMessage.includes("credential")
|
|
? "Authentication failed. Please check your credentials."
|
|
: message
|
|
|
|
return Response.json(
|
|
{
|
|
error: safeMessage,
|
|
...(isDev && {
|
|
details: message,
|
|
stack: error instanceof Error ? error.stack : undefined,
|
|
}),
|
|
},
|
|
{ status },
|
|
)
|
|
}
|
|
|
|
// Wrap handler with error handling
|
|
async function safeHandler(req: Request): Promise<Response> {
|
|
try {
|
|
return await handleChatRequest(req)
|
|
} catch (error) {
|
|
return handleError(error)
|
|
}
|
|
}
|
|
|
|
// Wrap with Langfuse observe (if configured)
|
|
const observedHandler = wrapWithObserve(safeHandler)
|
|
|
|
export async function POST(req: Request) {
|
|
return observedHandler(req)
|
|
}
|