mirror of
https://github.com/sst/opencode.git
synced 2025-08-22 14:04:07 +00:00
wip gateway
This commit is contained in:
Frank
parent
542186aa49
commit
20e818ad05
2 changed files with 445 additions and 451 deletions
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@ -178,456 +178,470 @@ const RestAuth: MiddlewareHandler = async (c, next) => {
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const app = new Hono<{ Bindings: Env; Variables: { keyRecord?: { id: string; workspaceID: string } } }>()
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.get("/", (c) => c.text("Hello, world!"))
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.post("/v1/chat/completions", GatewayAuth, async (c) => {
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try {
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const body = await c.req.json<ChatCompletionCreateParamsBase>()
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const keyRecord = c.get("keyRecord")!
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const model = SUPPORTED_MODELS[body.model as keyof typeof SUPPORTED_MODELS]?.model()
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if (!model) throw new Error(`Unsupported model: ${body.model}`)
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const requestBody = transformOpenAIRequestToAiSDK()
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return body.stream ? await handleStream() : await handleGenerate()
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async function handleStream() {
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const result = await model.doStream({
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...requestBody,
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})
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const encoder = new TextEncoder()
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const stream = new ReadableStream({
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async start(controller) {
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const id = `chatcmpl-${Date.now()}`
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const created = Math.floor(Date.now() / 1000)
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try {
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for await (const chunk of result.stream) {
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console.log("!!! CHUNK !!! : " + chunk.type)
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switch (chunk.type) {
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case "text-delta": {
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const data = {
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id,
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object: "chat.completion.chunk",
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created,
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model: body.model,
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choices: [
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{
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index: 0,
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delta: {
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content: chunk.delta,
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},
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finish_reason: null,
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},
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],
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}
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
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break
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}
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case "reasoning-delta": {
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const data = {
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id,
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object: "chat.completion.chunk",
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created,
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model: body.model,
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choices: [
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{
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index: 0,
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delta: {
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reasoning_content: chunk.delta,
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},
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finish_reason: null,
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},
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],
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}
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
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break
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}
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case "tool-call": {
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const data = {
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id,
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object: "chat.completion.chunk",
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created,
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model: body.model,
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choices: [
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{
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index: 0,
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delta: {
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tool_calls: [
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{
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index: 0,
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id: chunk.toolCallId,
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type: "function",
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function: {
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name: chunk.toolName,
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arguments: chunk.input,
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},
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},
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],
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},
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finish_reason: null,
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},
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],
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}
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
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break
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}
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case "error": {
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const data = {
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id,
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object: "chat.completion.chunk",
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created,
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model: body.model,
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choices: [
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{
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index: 0,
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delta: {},
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finish_reason: "stop",
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},
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],
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error: {
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message: typeof chunk.error === "string" ? chunk.error : chunk.error,
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type: "server_error",
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},
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}
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
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controller.enqueue(encoder.encode("data: [DONE]\n\n"))
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controller.close()
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break
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}
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case "finish": {
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const data = {
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id,
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object: "chat.completion.chunk",
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created,
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model: body.model,
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choices: [
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{
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index: 0,
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delta: {},
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finish_reason:
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{
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stop: "stop",
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length: "length",
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"content-filter": "content_filter",
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"tool-calls": "tool_calls",
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error: "stop",
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other: "stop",
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unknown: "stop",
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}[chunk.finishReason] || "stop",
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},
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],
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usage: {
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prompt_tokens: chunk.usage.inputTokens,
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completion_tokens: chunk.usage.outputTokens,
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total_tokens: chunk.usage.totalTokens,
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completion_tokens_details: {
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reasoning_tokens: chunk.usage.reasoningTokens,
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},
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prompt_tokens_details: {
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cached_tokens: chunk.usage.cachedInputTokens,
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},
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},
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}
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await trackUsage(body.model, chunk.usage, chunk.providerMetadata)
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
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controller.enqueue(encoder.encode("data: [DONE]\n\n"))
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controller.close()
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break
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}
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//case "stream-start":
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//case "response-metadata":
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case "text-start":
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case "text-end":
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case "reasoning-start":
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case "reasoning-end":
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case "tool-input-start":
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case "tool-input-delta":
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case "tool-input-end":
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case "raw":
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default:
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// Log unknown chunk types for debugging
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console.warn(`Unknown chunk type: ${(chunk as any).type}`)
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break
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}
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}
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} catch (error) {
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controller.error(error)
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}
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},
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})
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return new Response(stream, {
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headers: {
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"Content-Type": "text/plain; charset=utf-8",
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"Cache-Control": "no-cache",
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Connection: "keep-alive",
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},
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})
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}
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async function handleGenerate() {
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const response = await model.doGenerate({
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...requestBody,
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})
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await trackUsage(body.model, response.usage, response.providerMetadata)
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return c.json({
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id: `chatcmpl-${Date.now()}`,
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object: "chat.completion" as const,
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created: Math.floor(Date.now() / 1000),
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model: body.model,
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choices: [
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return await Actor.provide("system", { workspaceID: keyRecord.workspaceID }, async () => {
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try {
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// Check balance
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const customer = await Billing.get()
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if (customer.balance <= 0) {
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return c.json(
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{
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index: 0,
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message: {
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role: "assistant" as const,
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content: response.content?.find((c) => c.type === "text")?.text ?? "",
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reasoning_content: response.content?.find((c) => c.type === "reasoning")?.text,
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tool_calls: response.content
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?.filter((c) => c.type === "tool-call")
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.map((toolCall) => ({
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id: toolCall.toolCallId,
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type: "function" as const,
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function: {
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name: toolCall.toolName,
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arguments: toolCall.input,
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},
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})),
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error: {
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message: "Insufficient balance",
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type: "insufficient_quota",
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param: null,
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code: "insufficient_quota",
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},
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finish_reason:
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(
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{
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stop: "stop",
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length: "length",
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"content-filter": "content_filter",
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"tool-calls": "tool_calls",
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error: "stop",
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other: "stop",
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unknown: "stop",
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} as const
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)[response.finishReason] || "stop",
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},
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],
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usage: {
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prompt_tokens: response.usage?.inputTokens,
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completion_tokens: response.usage?.outputTokens,
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total_tokens: response.usage?.totalTokens,
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completion_tokens_details: {
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reasoning_tokens: response.usage?.reasoningTokens,
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},
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prompt_tokens_details: {
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cached_tokens: response.usage?.cachedInputTokens,
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},
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},
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})
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}
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function transformOpenAIRequestToAiSDK() {
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const prompt = transformMessages()
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const tools = transformTools()
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return {
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prompt,
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maxOutputTokens: body.max_tokens ?? body.max_completion_tokens ?? undefined,
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temperature: body.temperature ?? undefined,
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topP: body.top_p ?? undefined,
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frequencyPenalty: body.frequency_penalty ?? undefined,
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presencePenalty: body.presence_penalty ?? undefined,
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providerOptions: body.reasoning_effort
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? {
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anthropic: {
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reasoningEffort: body.reasoning_effort,
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},
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}
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: undefined,
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stopSequences: (typeof body.stop === "string" ? [body.stop] : body.stop) ?? undefined,
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responseFormat: (() => {
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if (!body.response_format) return { type: "text" as const }
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if (body.response_format.type === "json_schema")
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return {
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type: "json" as const,
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schema: body.response_format.json_schema.schema,
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name: body.response_format.json_schema.name,
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description: body.response_format.json_schema.description,
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}
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if (body.response_format.type === "json_object") return { type: "json" as const }
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throw new Error("Unsupported response format")
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})(),
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seed: body.seed ?? undefined,
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tools: tools.tools,
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toolChoice: tools.toolChoice,
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401,
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)
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}
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function transformTools() {
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const { tools, tool_choice } = body
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const body = await c.req.json<ChatCompletionCreateParamsBase>()
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const model = SUPPORTED_MODELS[body.model as keyof typeof SUPPORTED_MODELS]?.model()
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if (!model) throw new Error(`Unsupported model: ${body.model}`)
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if (!tools || tools.length === 0) {
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return { tools: undefined, toolChoice: undefined }
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}
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const requestBody = transformOpenAIRequestToAiSDK()
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const aiSdkTools = tools.map((tool) => {
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return {
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type: tool.type,
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name: tool.function.name,
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description: tool.function.description,
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inputSchema: tool.function.parameters!,
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}
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return body.stream ? await handleStream() : await handleGenerate()
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async function handleStream() {
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const result = await model.doStream({
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...requestBody,
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})
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let aiSdkToolChoice
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if (tool_choice == null) {
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aiSdkToolChoice = undefined
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} else if (tool_choice === "auto") {
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aiSdkToolChoice = { type: "auto" as const }
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} else if (tool_choice === "none") {
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aiSdkToolChoice = { type: "none" as const }
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} else if (tool_choice === "required") {
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aiSdkToolChoice = { type: "required" as const }
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} else if (tool_choice.type === "function") {
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aiSdkToolChoice = {
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type: "tool" as const,
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toolName: tool_choice.function.name,
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}
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}
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const encoder = new TextEncoder()
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const stream = new ReadableStream({
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async start(controller) {
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const id = `chatcmpl-${Date.now()}`
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const created = Math.floor(Date.now() / 1000)
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return { tools: aiSdkTools, toolChoice: aiSdkToolChoice }
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}
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function transformMessages() {
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const { messages } = body
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const prompt: LanguageModelV2Prompt = []
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for (const message of messages) {
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switch (message.role) {
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case "system": {
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prompt.push({
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role: "system",
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content: message.content as string,
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})
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break
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}
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case "user": {
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if (typeof message.content === "string") {
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prompt.push({
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role: "user",
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content: [{ type: "text", text: message.content }],
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})
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} else {
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const content = message.content.map((part) => {
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switch (part.type) {
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case "text":
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return { type: "text" as const, text: part.text }
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case "image_url":
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return {
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type: "file" as const,
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mediaType: "image/jpeg" as const,
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data: part.image_url.url,
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}
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default:
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throw new Error(`Unsupported content part type: ${(part as any).type}`)
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try {
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for await (const chunk of result.stream) {
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console.log("!!! CHUNK !!! : " + chunk.type)
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switch (chunk.type) {
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case "text-delta": {
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const data = {
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id,
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object: "chat.completion.chunk",
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created,
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model: body.model,
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choices: [
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{
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index: 0,
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delta: {
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content: chunk.delta,
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},
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finish_reason: null,
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},
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],
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}
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
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break
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}
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})
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prompt.push({
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role: "user",
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content,
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})
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}
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break
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}
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case "assistant": {
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const content: Array<
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| { type: "text"; text: string }
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| {
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type: "tool-call"
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toolCallId: string
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toolName: string
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input: any
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case "reasoning-delta": {
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const data = {
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id,
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object: "chat.completion.chunk",
|
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created,
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model: body.model,
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choices: [
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{
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index: 0,
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delta: {
|
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reasoning_content: chunk.delta,
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},
|
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finish_reason: null,
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},
|
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],
|
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}
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
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break
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}
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> = []
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if (message.content) {
|
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content.push({
|
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type: "text",
|
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text: message.content as string,
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})
|
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}
|
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case "tool-call": {
|
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const data = {
|
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id,
|
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object: "chat.completion.chunk",
|
||||
created,
|
||||
model: body.model,
|
||||
choices: [
|
||||
{
|
||||
index: 0,
|
||||
delta: {
|
||||
tool_calls: [
|
||||
{
|
||||
index: 0,
|
||||
id: chunk.toolCallId,
|
||||
type: "function",
|
||||
function: {
|
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name: chunk.toolName,
|
||||
arguments: chunk.input,
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
finish_reason: null,
|
||||
},
|
||||
],
|
||||
}
|
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
|
||||
break
|
||||
}
|
||||
|
||||
if (message.tool_calls) {
|
||||
for (const toolCall of message.tool_calls) {
|
||||
content.push({
|
||||
type: "tool-call",
|
||||
toolCallId: toolCall.id,
|
||||
toolName: toolCall.function.name,
|
||||
input: JSON.parse(toolCall.function.arguments),
|
||||
})
|
||||
case "error": {
|
||||
const data = {
|
||||
id,
|
||||
object: "chat.completion.chunk",
|
||||
created,
|
||||
model: body.model,
|
||||
choices: [
|
||||
{
|
||||
index: 0,
|
||||
delta: {},
|
||||
finish_reason: "stop",
|
||||
},
|
||||
],
|
||||
error: {
|
||||
message: typeof chunk.error === "string" ? chunk.error : chunk.error,
|
||||
type: "server_error",
|
||||
},
|
||||
}
|
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controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
|
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controller.enqueue(encoder.encode("data: [DONE]\n\n"))
|
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controller.close()
|
||||
break
|
||||
}
|
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|
||||
case "finish": {
|
||||
const data = {
|
||||
id,
|
||||
object: "chat.completion.chunk",
|
||||
created,
|
||||
model: body.model,
|
||||
choices: [
|
||||
{
|
||||
index: 0,
|
||||
delta: {},
|
||||
finish_reason:
|
||||
{
|
||||
stop: "stop",
|
||||
length: "length",
|
||||
"content-filter": "content_filter",
|
||||
"tool-calls": "tool_calls",
|
||||
error: "stop",
|
||||
other: "stop",
|
||||
unknown: "stop",
|
||||
}[chunk.finishReason] || "stop",
|
||||
},
|
||||
],
|
||||
usage: {
|
||||
prompt_tokens: chunk.usage.inputTokens,
|
||||
completion_tokens: chunk.usage.outputTokens,
|
||||
total_tokens: chunk.usage.totalTokens,
|
||||
completion_tokens_details: {
|
||||
reasoning_tokens: chunk.usage.reasoningTokens,
|
||||
},
|
||||
prompt_tokens_details: {
|
||||
cached_tokens: chunk.usage.cachedInputTokens,
|
||||
},
|
||||
},
|
||||
}
|
||||
await trackUsage(body.model, chunk.usage, chunk.providerMetadata)
|
||||
controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
|
||||
controller.enqueue(encoder.encode("data: [DONE]\n\n"))
|
||||
controller.close()
|
||||
break
|
||||
}
|
||||
|
||||
//case "stream-start":
|
||||
//case "response-metadata":
|
||||
case "text-start":
|
||||
case "text-end":
|
||||
case "reasoning-start":
|
||||
case "reasoning-end":
|
||||
case "tool-input-start":
|
||||
case "tool-input-delta":
|
||||
case "tool-input-end":
|
||||
case "raw":
|
||||
default:
|
||||
// Log unknown chunk types for debugging
|
||||
console.warn(`Unknown chunk type: ${(chunk as any).type}`)
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
prompt.push({
|
||||
role: "assistant",
|
||||
content,
|
||||
})
|
||||
break
|
||||
} catch (error) {
|
||||
controller.error(error)
|
||||
}
|
||||
},
|
||||
})
|
||||
|
||||
case "tool": {
|
||||
prompt.push({
|
||||
role: "tool",
|
||||
content: [
|
||||
{
|
||||
type: "tool-result",
|
||||
toolName: "placeholder",
|
||||
toolCallId: message.tool_call_id,
|
||||
output: {
|
||||
type: "text",
|
||||
value: message.content as string,
|
||||
return new Response(stream, {
|
||||
headers: {
|
||||
"Content-Type": "text/plain; charset=utf-8",
|
||||
"Cache-Control": "no-cache",
|
||||
Connection: "keep-alive",
|
||||
},
|
||||
})
|
||||
}
|
||||
|
||||
async function handleGenerate() {
|
||||
const response = await model.doGenerate({
|
||||
...requestBody,
|
||||
})
|
||||
await trackUsage(body.model, response.usage, response.providerMetadata)
|
||||
return c.json({
|
||||
id: `chatcmpl-${Date.now()}`,
|
||||
object: "chat.completion" as const,
|
||||
created: Math.floor(Date.now() / 1000),
|
||||
model: body.model,
|
||||
choices: [
|
||||
{
|
||||
index: 0,
|
||||
message: {
|
||||
role: "assistant" as const,
|
||||
content: response.content?.find((c) => c.type === "text")?.text ?? "",
|
||||
reasoning_content: response.content?.find((c) => c.type === "reasoning")?.text,
|
||||
tool_calls: response.content
|
||||
?.filter((c) => c.type === "tool-call")
|
||||
.map((toolCall) => ({
|
||||
id: toolCall.toolCallId,
|
||||
type: "function" as const,
|
||||
function: {
|
||||
name: toolCall.toolName,
|
||||
arguments: toolCall.input,
|
||||
},
|
||||
},
|
||||
],
|
||||
})
|
||||
break
|
||||
}
|
||||
})),
|
||||
},
|
||||
finish_reason:
|
||||
(
|
||||
{
|
||||
stop: "stop",
|
||||
length: "length",
|
||||
"content-filter": "content_filter",
|
||||
"tool-calls": "tool_calls",
|
||||
error: "stop",
|
||||
other: "stop",
|
||||
unknown: "stop",
|
||||
} as const
|
||||
)[response.finishReason] || "stop",
|
||||
},
|
||||
],
|
||||
usage: {
|
||||
prompt_tokens: response.usage?.inputTokens,
|
||||
completion_tokens: response.usage?.outputTokens,
|
||||
total_tokens: response.usage?.totalTokens,
|
||||
completion_tokens_details: {
|
||||
reasoning_tokens: response.usage?.reasoningTokens,
|
||||
},
|
||||
prompt_tokens_details: {
|
||||
cached_tokens: response.usage?.cachedInputTokens,
|
||||
},
|
||||
},
|
||||
})
|
||||
}
|
||||
|
||||
default: {
|
||||
throw new Error(`Unsupported message role: ${message.role}`)
|
||||
}
|
||||
}
|
||||
function transformOpenAIRequestToAiSDK() {
|
||||
const prompt = transformMessages()
|
||||
const tools = transformTools()
|
||||
|
||||
return {
|
||||
prompt,
|
||||
maxOutputTokens: body.max_tokens ?? body.max_completion_tokens ?? undefined,
|
||||
temperature: body.temperature ?? undefined,
|
||||
topP: body.top_p ?? undefined,
|
||||
frequencyPenalty: body.frequency_penalty ?? undefined,
|
||||
presencePenalty: body.presence_penalty ?? undefined,
|
||||
providerOptions: body.reasoning_effort
|
||||
? {
|
||||
anthropic: {
|
||||
reasoningEffort: body.reasoning_effort,
|
||||
},
|
||||
}
|
||||
: undefined,
|
||||
stopSequences: (typeof body.stop === "string" ? [body.stop] : body.stop) ?? undefined,
|
||||
responseFormat: (() => {
|
||||
if (!body.response_format) return { type: "text" as const }
|
||||
if (body.response_format.type === "json_schema")
|
||||
return {
|
||||
type: "json" as const,
|
||||
schema: body.response_format.json_schema.schema,
|
||||
name: body.response_format.json_schema.name,
|
||||
description: body.response_format.json_schema.description,
|
||||
}
|
||||
if (body.response_format.type === "json_object") return { type: "json" as const }
|
||||
throw new Error("Unsupported response format")
|
||||
})(),
|
||||
seed: body.seed ?? undefined,
|
||||
tools: tools.tools,
|
||||
toolChoice: tools.toolChoice,
|
||||
}
|
||||
|
||||
return prompt
|
||||
function transformTools() {
|
||||
const { tools, tool_choice } = body
|
||||
|
||||
if (!tools || tools.length === 0) {
|
||||
return { tools: undefined, toolChoice: undefined }
|
||||
}
|
||||
|
||||
const aiSdkTools = tools.map((tool) => {
|
||||
return {
|
||||
type: tool.type,
|
||||
name: tool.function.name,
|
||||
description: tool.function.description,
|
||||
inputSchema: tool.function.parameters!,
|
||||
}
|
||||
})
|
||||
|
||||
let aiSdkToolChoice
|
||||
if (tool_choice == null) {
|
||||
aiSdkToolChoice = undefined
|
||||
} else if (tool_choice === "auto") {
|
||||
aiSdkToolChoice = { type: "auto" as const }
|
||||
} else if (tool_choice === "none") {
|
||||
aiSdkToolChoice = { type: "none" as const }
|
||||
} else if (tool_choice === "required") {
|
||||
aiSdkToolChoice = { type: "required" as const }
|
||||
} else if (tool_choice.type === "function") {
|
||||
aiSdkToolChoice = {
|
||||
type: "tool" as const,
|
||||
toolName: tool_choice.function.name,
|
||||
}
|
||||
}
|
||||
|
||||
return { tools: aiSdkTools, toolChoice: aiSdkToolChoice }
|
||||
}
|
||||
|
||||
function transformMessages() {
|
||||
const { messages } = body
|
||||
const prompt: LanguageModelV2Prompt = []
|
||||
|
||||
for (const message of messages) {
|
||||
switch (message.role) {
|
||||
case "system": {
|
||||
prompt.push({
|
||||
role: "system",
|
||||
content: message.content as string,
|
||||
})
|
||||
break
|
||||
}
|
||||
|
||||
case "user": {
|
||||
if (typeof message.content === "string") {
|
||||
prompt.push({
|
||||
role: "user",
|
||||
content: [{ type: "text", text: message.content }],
|
||||
})
|
||||
} else {
|
||||
const content = message.content.map((part) => {
|
||||
switch (part.type) {
|
||||
case "text":
|
||||
return { type: "text" as const, text: part.text }
|
||||
case "image_url":
|
||||
return {
|
||||
type: "file" as const,
|
||||
mediaType: "image/jpeg" as const,
|
||||
data: part.image_url.url,
|
||||
}
|
||||
default:
|
||||
throw new Error(`Unsupported content part type: ${(part as any).type}`)
|
||||
}
|
||||
})
|
||||
prompt.push({
|
||||
role: "user",
|
||||
content,
|
||||
})
|
||||
}
|
||||
break
|
||||
}
|
||||
|
||||
case "assistant": {
|
||||
const content: Array<
|
||||
| { type: "text"; text: string }
|
||||
| {
|
||||
type: "tool-call"
|
||||
toolCallId: string
|
||||
toolName: string
|
||||
input: any
|
||||
}
|
||||
> = []
|
||||
|
||||
if (message.content) {
|
||||
content.push({
|
||||
type: "text",
|
||||
text: message.content as string,
|
||||
})
|
||||
}
|
||||
|
||||
if (message.tool_calls) {
|
||||
for (const toolCall of message.tool_calls) {
|
||||
content.push({
|
||||
type: "tool-call",
|
||||
toolCallId: toolCall.id,
|
||||
toolName: toolCall.function.name,
|
||||
input: JSON.parse(toolCall.function.arguments),
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
prompt.push({
|
||||
role: "assistant",
|
||||
content,
|
||||
})
|
||||
break
|
||||
}
|
||||
|
||||
case "tool": {
|
||||
prompt.push({
|
||||
role: "tool",
|
||||
content: [
|
||||
{
|
||||
type: "tool-result",
|
||||
toolName: "placeholder",
|
||||
toolCallId: message.tool_call_id,
|
||||
output: {
|
||||
type: "text",
|
||||
value: message.content as string,
|
||||
},
|
||||
},
|
||||
],
|
||||
})
|
||||
break
|
||||
}
|
||||
|
||||
default: {
|
||||
throw new Error(`Unsupported message role: ${message.role}`)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return prompt
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
async function trackUsage(model: string, usage: LanguageModelUsage, providerMetadata?: ProviderMetadata) {
|
||||
const keyRecord = c.get("keyRecord")
|
||||
if (!keyRecord) return
|
||||
async function trackUsage(model: string, usage: LanguageModelUsage, providerMetadata?: ProviderMetadata) {
|
||||
const modelData = SUPPORTED_MODELS[model as keyof typeof SUPPORTED_MODELS]
|
||||
if (!modelData) throw new Error(`Unsupported model: ${model}`)
|
||||
|
||||
const modelData = SUPPORTED_MODELS[model as keyof typeof SUPPORTED_MODELS]
|
||||
if (!modelData) throw new Error(`Unsupported model: ${model}`)
|
||||
const inputTokens = usage.inputTokens ?? 0
|
||||
const outputTokens = usage.outputTokens ?? 0
|
||||
const reasoningTokens = usage.reasoningTokens ?? 0
|
||||
const cacheReadTokens = usage.cachedInputTokens ?? 0
|
||||
const cacheWriteTokens =
|
||||
providerMetadata?.["anthropic"]?.["cacheCreationInputTokens"] ??
|
||||
// @ts-expect-error
|
||||
providerMetadata?.["bedrock"]?.["usage"]?.["cacheWriteInputTokens"] ??
|
||||
0
|
||||
|
||||
const inputTokens = usage.inputTokens ?? 0
|
||||
const outputTokens = usage.outputTokens ?? 0
|
||||
const reasoningTokens = usage.reasoningTokens ?? 0
|
||||
const cacheReadTokens = usage.cachedInputTokens ?? 0
|
||||
const cacheWriteTokens =
|
||||
providerMetadata?.["anthropic"]?.["cacheCreationInputTokens"] ??
|
||||
// @ts-expect-error
|
||||
providerMetadata?.["bedrock"]?.["usage"]?.["cacheWriteInputTokens"] ??
|
||||
0
|
||||
const inputCost = modelData.input * inputTokens
|
||||
const outputCost = modelData.output * outputTokens
|
||||
const reasoningCost = modelData.reasoning * reasoningTokens
|
||||
const cacheReadCost = modelData.cacheRead * cacheReadTokens
|
||||
const cacheWriteCost = modelData.cacheWrite * cacheWriteTokens
|
||||
const costInCents = (inputCost + outputCost + reasoningCost + cacheReadCost + cacheWriteCost) * 100
|
||||
|
||||
const inputCost = modelData.input * inputTokens
|
||||
const outputCost = modelData.output * outputTokens
|
||||
const reasoningCost = modelData.reasoning * reasoningTokens
|
||||
const cacheReadCost = modelData.cacheRead * cacheReadTokens
|
||||
const cacheWriteCost = modelData.cacheWrite * cacheWriteTokens
|
||||
const costInCents = (inputCost + outputCost + reasoningCost + cacheReadCost + cacheWriteCost) * 100
|
||||
|
||||
await Actor.provide("system", { workspaceID: keyRecord.workspaceID }, async () => {
|
||||
await Billing.consume({
|
||||
model,
|
||||
inputTokens,
|
||||
|
|
@ -637,18 +651,18 @@ const app = new Hono<{ Bindings: Env; Variables: { keyRecord?: { id: string; wor
|
|||
cacheWriteTokens,
|
||||
costInCents,
|
||||
})
|
||||
})
|
||||
|
||||
await Database.use((tx) =>
|
||||
tx
|
||||
.update(KeyTable)
|
||||
.set({ timeUsed: sql`now()` })
|
||||
.where(eq(KeyTable.id, keyRecord.id)),
|
||||
)
|
||||
await Database.use((tx) =>
|
||||
tx
|
||||
.update(KeyTable)
|
||||
.set({ timeUsed: sql`now()` })
|
||||
.where(eq(KeyTable.id, keyRecord.id)),
|
||||
)
|
||||
}
|
||||
} catch (error: any) {
|
||||
return c.json({ error: { message: error.message } }, 500)
|
||||
}
|
||||
} catch (error: any) {
|
||||
return c.json({ error: { message: error.message } }, 500)
|
||||
}
|
||||
})
|
||||
})
|
||||
.use("/*", cors())
|
||||
.use(RestAuth)
|
||||
|
|
|
|||
|
|
@ -1,25 +1,5 @@
|
|||
{
|
||||
"$schema": "https://opencode.ai/config.json",
|
||||
"provider": {
|
||||
"oc-frank": {
|
||||
"npm": "@ai-sdk/openai-compatible",
|
||||
"name": "OC-Frank",
|
||||
"options": {
|
||||
"baseURL": "https://api.gateway.frank.dev.opencode.ai/v1"
|
||||
},
|
||||
"models": {
|
||||
"anthropic/claude-sonnet-4": {
|
||||
"name": "Claude Sonnet 4"
|
||||
},
|
||||
"openai/gpt-4.1": {
|
||||
"name": "GPT-4.1"
|
||||
},
|
||||
"zhipuai/glm-4.5-flash": {
|
||||
"name": "GLM-4.5 Flash"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"mcp": {
|
||||
"context7": {
|
||||
"type": "remote",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue