next-ai-draw-io/edge-functions/api/edgeai/chat/completions.ts

/**
 * EdgeOne Pages Edge Function for OpenAI-compatible Chat Completions API
 *
 * This endpoint provides an OpenAI-compatible API that can be used with
 * AI SDK's createOpenAI({ baseURL: '/api/edgeai' })
 *
 * Uses EdgeOne Edge AI's AI.chatCompletions() which now supports native tool calling.
 */

import { z } from "zod"

// EdgeOne Pages global AI object
declare const AI: {
    chatCompletions(options: {
        model: string
        messages: Array<{ role: string; content: string | null }>
        stream?: boolean
        max_tokens?: number
        temperature?: number
        tools?: any
        tool_choice?: any
    }): Promise<ReadableStream<Uint8Array> | any>
}

const messageItemSchema = z
    .object({
        role: z.enum(["user", "assistant", "system", "tool", "function"]),
        content: z.string().nullable().optional(),
    })
    .passthrough()

const messageSchema = z
    .object({
        messages: z.array(messageItemSchema),
        model: z.string().optional(),
        stream: z.boolean().optional(),
        tools: z.any().optional(),
        tool_choice: z.any().optional(),
        functions: z.any().optional(),
        function_call: z.any().optional(),
        temperature: z.number().optional(),
        top_p: z.number().optional(),
        max_tokens: z.number().optional(),
        presence_penalty: z.number().optional(),
        frequency_penalty: z.number().optional(),
        stop: z.union([z.string(), z.array(z.string())]).optional(),
        response_format: z.any().optional(),
        seed: z.number().optional(),
        user: z.string().optional(),
        n: z.number().int().optional(),
        logit_bias: z.record(z.string(), z.number()).optional(),
        parallel_tool_calls: z.boolean().optional(),
        stream_options: z.any().optional(),
    })
    .passthrough()

// Model configuration
const ALLOWED_MODELS = [
    "@tx/deepseek-ai/deepseek-v32",
    "@tx/deepseek-ai/deepseek-r1-0528",
    "@tx/deepseek-ai/deepseek-v3-0324",
]

const MODEL_ALIASES: Record<string, string> = {
    "deepseek-v3.2": "@tx/deepseek-ai/deepseek-v32",
    "deepseek-r1-0528": "@tx/deepseek-ai/deepseek-r1-0528",
    "deepseek-v3-0324": "@tx/deepseek-ai/deepseek-v3-0324",
}

/**
 * Create standardized JSON response
 */
function createResponse(body: any, status = 200, extraHeaders = {}): Response {
    return new Response(JSON.stringify(body), {
        status,
        headers: {
            "Content-Type": "application/json",
            ...extraHeaders,
        },
    })
}

// Only the app's own server (/api/chat, /api/validate-model) calls this
// function, so no CORS headers are sent: other sites' pages can't call it
// from a browser and spend the deployment's Edge AI quota.
// Same rule as lib/access-code.ts, but reading the edge function's env.
// No codes configured (or env unavailable) means no check.
function hasValidAccessCode(request: Request, env: any): boolean {
    const accessCodes: string[] =
        env?.ACCESS_CODE_LIST?.split(",")
            .map((code: string) => code.trim())
            .filter(Boolean) || []
    if (accessCodes.length === 0) return true
    const accessCode = request.headers.get("x-access-code")
    return !!accessCode && accessCodes.includes(accessCode)
}

export async function onRequest({ request, env }: any) {
    // Requiring JSON also makes any cross-site browser request need a CORS
    // preflight, which fails without CORS headers
    if (
        request.method !== "POST" ||
        !request.headers.get("content-type")?.includes("application/json")
    ) {
        return createResponse(
            {
                error: {
                    message: "Expected a POST request with a JSON body",
                    type: "invalid_request_error",
                },
            },
            400,
        )
    }

    if (!hasValidAccessCode(request, env)) {
        return createResponse(
            {
                error: {
                    message: "Invalid or missing access code",
                    type: "invalid_request_error",
                },
            },
            401,
        )
    }

    request.headers.delete("accept-encoding")

    try {
        const json = await request.clone().json()
        const parseResult = messageSchema.safeParse(json)

        if (!parseResult.success) {
            return createResponse(
                {
                    error: {
                        message: parseResult.error.message,
                        type: "invalid_request_error",
                    },
                },
                400,
            )
        }

        const { messages, model, stream, tools, tool_choice, ...extraParams } =
            parseResult.data

        // Validate messages
        const userMessages = messages.filter(
            (message) => message.role === "user",
        )
        if (!userMessages.length) {
            return createResponse(
                {
                    error: {
                        message: "No user message found",
                        type: "invalid_request_error",
                    },
                },
                400,
            )
        }

        // Resolve model
        const requestedModel = model || ALLOWED_MODELS[0]
        const selectedModel = MODEL_ALIASES[requestedModel] || requestedModel

        if (!ALLOWED_MODELS.includes(selectedModel)) {
            return createResponse(
                {
                    error: {
                        message: `Invalid model: ${requestedModel}.`,
                        type: "invalid_request_error",
                    },
                },
                400,
            )
        }

        console.log(
            `[EdgeOne] Model: ${selectedModel}, Tools: ${tools?.length || 0}, Stream: ${stream ?? true}`,
        )

        try {
            const isStream = !!stream

            // Non-streaming: return mock response for validation
            // AI.chatCompletions doesn't support non-streaming mode
            if (!isStream) {
                const mockResponse = {
                    id: `chatcmpl-${Date.now()}`,
                    object: "chat.completion",
                    created: Math.floor(Date.now() / 1000),
                    model: selectedModel,
                    choices: [
                        {
                            index: 0,
                            message: {
                                role: "assistant",
                                content: "OK",
                            },
                            finish_reason: "stop",
                        },
                    ],
                    usage: {
                        prompt_tokens: 10,
                        completion_tokens: 1,
                        total_tokens: 11,
                    },
                }
                return createResponse(mockResponse)
            }

            // Build AI.chatCompletions options for streaming
            const aiOptions: any = {
                ...extraParams,
                model: selectedModel,
                messages,
                stream: true,
            }

            // Add tools if provided
            if (tools && tools.length > 0) {
                aiOptions.tools = tools
            }
            if (tool_choice !== undefined) {
                aiOptions.tool_choice = tool_choice
            }

            const aiResponse = await AI.chatCompletions(aiOptions)

            // Streaming response
            return new Response(aiResponse, {
                headers: {
                    "Content-Type": "text/event-stream; charset=utf-8",
                    "Cache-Control": "no-cache, no-store, no-transform",
                    "X-Accel-Buffering": "no",
                    Connection: "keep-alive",
                },
            })
        } catch (error: any) {
            // Handle EdgeOne specific errors
            try {
                const message = JSON.parse(error.message)
                if (message.code === 14020) {
                    return createResponse(
                        {
                            error: {
                                message:
                                    "The daily public quota has been exhausted. After deployment, you can enjoy a personal daily exclusive quota.",
                                type: "rate_limit_error",
                            },
                        },
                        429,
                    )
                }
                return createResponse(
                    { error: { message: error.message, type: "api_error" } },
                    500,
                )
            } catch {
                // Not a JSON error message
            }

            console.error("[EdgeOne] AI error:", error.message)
            return createResponse(
                {
                    error: {
                        message: error.message || "AI service error",
                        type: "api_error",
                    },
                },
                500,
            )
        }
    } catch (error: any) {
        console.error("[EdgeOne] Request error:", error.message)
        return createResponse(
            {
                error: {
                    message: "Request processing failed",
                    type: "server_error",
                    details: error.message,
                },
            },
            500,
        )
    }
}