mirror of
https://github.com/DayuanJiang/next-ai-draw-io.git
synced 2026-10-05 01:07:56 +08:00
dayuan.jiang
0855b35ff2
Found by the PR review, each with a test that failed first: - Quota: any key header skipped it, even one the provider never reads (x-aws-access-key-id with OpenAI), so a request ran on the server's key without being counted. The check now runs after the model is resolved and uses usesServerCredentials. On main already. - usesServerCredentials read the raw base URL; "/" cleans up to none, so an Ollama request ran on the server's key past the server-model check. - SGLang's default 127.0.0.1:8000 only fills the settings form. Chat and the model list used it as a real address, so the server called its own machine even with private URLs blocked. Now a base URL is required. - With a user's OpenAI key and no base URL, the SDK read the server's OPENAI_BASE_URL. The official endpoint is now passed. On main already. - The Test button refused nothing on the server's keys (Ollama Cloud), and a 15 s timeout reported "connected, no tool call". - The model list for Ollama without a base URL came from ollama.com while chat went to the server's Ollama. - Bedrock's "Too many tokens, please wait" counted as context too long. - On the server's keys the provider's error text stays in the server log; it can name the server's AWS account, role or internal hosts. - Desktop app: the preset keys are the user's own (NEXT_AI_DRAWIO_DESKTOP), so Max Output Tokens can be raised and keyless models in settings work again. A launch that found the remembered port taken no longer replaces it, which hid the user's chats and settings for good.
781 lines
31 KiB
TypeScript
781 lines
31 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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stepCountIs,
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streamText,
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} from "ai"
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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 { checkAccessCode } from "@/lib/access-code"
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import {
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CACHE_POINT,
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getAIModel,
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SINGLE_SYSTEM_PROVIDERS,
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supportsPromptCaching,
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usesServerCredentials,
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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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dropInvalidToolCalls,
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fixToolInputJson,
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replaceHistoricalToolInputs,
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validateFileParts,
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} from "@/lib/chat-helpers"
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import { withDeprecatedParamsFallback } from "@/lib/deprecated-params"
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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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endTrace,
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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 { classifyLLMError, streamErrorText } from "@/lib/llm-errors"
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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 {
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type FlattenedServerModel,
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findServerModelById,
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} from "@/lib/server-model-config"
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import { allowPrivateUrls, isPrivateUrl } from "@/lib/ssrf-protection"
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import { getSystemPrompt } from "@/lib/system-prompts"
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import { getUserIdFromRequest } from "@/lib/user-id"
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import { hasCells } from "@/packages/mcp-server/src/pages.ts"
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import {
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getShapeLibrary,
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SHAPE_LIBRARY_LIST,
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} from "@/packages/mcp-server/src/shape-library.ts"
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import { SWIMLANE_EXAMPLE } from "@/packages/mcp-server/src/xml-examples.ts"
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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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// Responses streamed from the model, whose trace streamText's callbacks end
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const modelStreamResponses = new WeakSet<Response>()
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// Inner handler function
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const DEBUG_LLM_PAYLOAD = process.env.DEBUG_LLM_PAYLOAD === "true"
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async function handleChatRequest(req: Request): Promise<Response> {
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// Check for access code
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const accessDenied = checkAccessCode(req)
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if (accessDenied) return accessDenied
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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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// === 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 || !hasCells(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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// Same rule as validate-model: with ALLOW_PRIVATE_URLS=false a request may
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// not point the server at a private or internal address
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if (baseUrl && !allowPrivateUrls() && (await isPrivateUrl(baseUrl))) {
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return Response.json(
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{ error: "Private or internal base URLs are not allowed." },
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{ status: 400 },
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)
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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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let serverModel: FlattenedServerModel | null = null
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if (selectedModelId?.startsWith("server:")) {
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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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// A server model runs the model it was configured with, whatever the header says
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modelId: serverModel?.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, and the access code,
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// which the EdgeOne function checks too
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...(provider === "edgeone" && {
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headers: {
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...(cookieHeader && { cookie: cookieHeader }),
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"x-access-code": req.headers.get("x-access-code") || "",
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},
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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: ${clientOverrides.modelId}`,
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)
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// Get AI model with optional client overrides
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const {
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model: baseModel,
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providerOptions,
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modelId,
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provider: resolvedProvider,
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} = getAIModel(clientOverrides)
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// On the server's own keys, only run models the server offers: a server
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// model picked by id (its model name is fixed above) or one in AI_MODEL.
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// With their own key, users can run any model.
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const onServerCredentials = usesServerCredentials(
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resolvedProvider,
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clientOverrides,
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)
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const envModels =
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process.env.AI_MODEL?.split(",").map((m) => m.trim()) || []
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if (onServerCredentials && !serverModel && !envModels.includes(modelId)) {
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return Response.json(
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{
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error: `Model "${modelId}" is not available on this server. Add your own API key in Settings to use it.`,
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},
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{ status: 400 },
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)
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}
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// === SERVER-SIDE QUOTA CHECK START ===
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// Quota is opt-in (DYNAMODB_QUOTA_TABLE) and counts what runs on the
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// server's keys. Decided by the key actually used: a key header the
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// provider never reads must not skip it.
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const countsQuota =
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isQuotaEnabled() && onServerCredentials && userId !== "anonymous"
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if (countsQuota) {
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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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// Retry once if the provider rejects the requested budget, or (newer
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// Claude models) the sampling or thinking settings
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const model = withOutputTokenLimitFallback(
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withDeprecatedParamsFallback(baseModel),
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)
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// The user setting can raise the budget only on their own key (in the
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// desktop app every key is the user's); on the server's keys it can only
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// lower it
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const maxOutputTokens = resolveMaxOutputTokens(
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req.headers.get("x-max-output-tokens"),
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onServerCredentials,
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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_LLM_PAYLOAD=true logs the incoming message structure
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if (DEBUG_LLM_PAYLOAD) {
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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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}
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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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// and their results. Bedrock API rejects messages where toolUse.input is not a valid
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// JSON object, and every provider rejects a tool result whose call is gone.
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enhancedMessages = dropInvalidToolCalls(enhancedMessages)
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// DEBUG_LLM_PAYLOAD=true logs what is sent to the model
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if (DEBUG_LLM_PAYLOAD) {
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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 (
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part.type === "tool-call" ||
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part.type === "tool-result"
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) {
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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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}
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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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mediaType: 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: CACHE_POINT,
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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 =
|
|
SINGLE_SYSTEM_PROVIDERS.has(resolvedProvider) || isCustomOpenAIEndpoint
|
|
|
|
const xmlContext = `${
|
|
previousXml
|
|
? `Previous diagram XML (before user's last message):
|
|
"""xml
|
|
${previousXml}
|
|
"""
|
|
|
|
`
|
|
: ""
|
|
}Current diagram XML (AUTHORITATIVE - the source of truth):
|
|
"""xml
|
|
${xml || ""}
|
|
"""
|
|
|
|
IMPORTANT: The "Current diagram XML" is the SINGLE SOURCE OF TRUTH for what's on the canvas right now. The user can manually add, delete, or modify shapes directly in draw.io. Always count and describe elements based on the CURRENT XML, not on what you previously generated. If both previous and current XML are shown, compare them to understand what the user changed.`
|
|
|
|
const systemMessages = isSingleSystemProvider
|
|
? [
|
|
{
|
|
role: "system" as const,
|
|
content: `${finalSystemMessage}\n\n${xmlContext}`,
|
|
},
|
|
]
|
|
: [
|
|
// Cache breakpoint 1: Instructions (+ optional custom instructions)
|
|
{
|
|
role: "system" as const,
|
|
content: finalSystemMessage,
|
|
...(shouldCache && { providerOptions: CACHE_POINT }),
|
|
},
|
|
// Cache breakpoint 2: Previous and Current diagram XML context
|
|
{
|
|
role: "system" as const,
|
|
content: xmlContext,
|
|
...(shouldCache && { providerOptions: CACHE_POINT }),
|
|
},
|
|
]
|
|
|
|
const allMessages = [...systemMessages, ...enhancedMessages]
|
|
|
|
const result = streamText({
|
|
model,
|
|
// The system messages carry cache points, so they go in messages.
|
|
// A client's own system messages have string content and were
|
|
// dropped by the empty-content filter above.
|
|
allowSystemInMessages: true,
|
|
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,
|
|
// then use jsonrepair to fix truncated JSON
|
|
const repairedInput = jsonrepair(
|
|
fixToolInputJson(toolCall.input),
|
|
)
|
|
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,
|
|
)
|
|
// Keep the original error, so the model and the client see why
|
|
// the input was rejected and the model can retry the call
|
|
return null
|
|
}
|
|
}
|
|
// Don't attempt to repair other errors (like NoSuchToolError)
|
|
return null
|
|
},
|
|
messages: allMessages,
|
|
...(providerOptions && { providerOptions }), // This now includes all reasoning configs
|
|
// 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)
|
|
// inputTokens already includes cache reads and writes in AI SDK 6
|
|
if (countsQuota && totalUsage) {
|
|
const totalTokens =
|
|
(totalUsage.inputTokens || 0) +
|
|
(totalUsage.outputTokens || 0)
|
|
recordTokenUsage(userId, totalTokens)
|
|
}
|
|
},
|
|
// onFinish is skipped when the stream fails or is aborted, so end the trace here
|
|
onError: ({ error }) => {
|
|
console.error(error) // what AI SDK does without an onError
|
|
endTrace()
|
|
},
|
|
onAbort: () => endTrace(),
|
|
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):
|
|
${SWIMLANE_EXAMPLE}
|
|
|
|
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:
|
|
${SHAPE_LIBRARY_LIST}
|
|
|
|
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 }) => {
|
|
// Only known library names reach the file system
|
|
const result = await getShapeLibrary(
|
|
library,
|
|
path.join(process.cwd(), "docs/shape-libraries"),
|
|
)
|
|
return result.ok ? result.text : result.error
|
|
},
|
|
},
|
|
},
|
|
...(process.env.TEMPERATURE !== undefined && {
|
|
temperature: parseFloat(process.env.TEMPERATURE),
|
|
}),
|
|
})
|
|
|
|
const response = result.toUIMessageStreamResponse({
|
|
sendReasoning: true,
|
|
onError: (error) => streamErrorText(error, onServerCredentials),
|
|
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
|
|
},
|
|
})
|
|
modelStreamResponses.add(response)
|
|
return response
|
|
}
|
|
|
|
// Errors before the stream starts, as JSON the chat panel reads
|
|
function handleError(error: unknown): Response {
|
|
console.error("Error in chat route:", error)
|
|
|
|
const isDev = process.env.NODE_ENV === "development"
|
|
const classified = classifyLLMError(error)
|
|
const status =
|
|
(error as { statusCode?: number })?.statusCode ||
|
|
(error as { status?: number })?.status ||
|
|
(classified.code === "invalid_api_key" ? 401 : 500)
|
|
|
|
return Response.json(
|
|
{
|
|
...classified,
|
|
...(isDev && {
|
|
details: APICallError.isInstance(error)
|
|
? error.responseBody
|
|
: undefined,
|
|
stack: error instanceof Error ? error.stack : undefined,
|
|
}),
|
|
},
|
|
{ status },
|
|
)
|
|
}
|
|
|
|
// Wrap handler with error handling
|
|
async function safeHandler(req: Request): Promise<Response> {
|
|
let response: Response
|
|
try {
|
|
response = await handleChatRequest(req)
|
|
} catch (error) {
|
|
response = handleError(error)
|
|
}
|
|
// Early returns, cache hits and errors never reach streamText's callbacks,
|
|
// so their Langfuse trace has to be ended here
|
|
if (!modelStreamResponses.has(response)) endTrace()
|
|
return response
|
|
}
|
|
|
|
// Wrap with Langfuse observe (if configured)
|
|
const observedHandler = wrapWithObserve(safeHandler)
|
|
|
|
export async function POST(req: Request) {
|
|
return observedHandler(req)
|
|
}
|