opencode/packages/function/src/gateway.ts
Frank 40036abb9d
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2025-08-04 21:08:29 -04:00

499 lines
17 KiB
TypeScript

import { Hono, Context, Next } from "hono"
import { Resource } from "sst"
import { generateText, streamText } from "ai"
import { createAnthropic } from "@ai-sdk/anthropic"
import { createOpenAI } from "@ai-sdk/openai"
import { createOpenAICompatible } from "@ai-sdk/openai-compatible"
import { type LanguageModelV2Prompt } from "@ai-sdk/provider"
import { type ChatCompletionCreateParamsBase } from "openai/resources/chat/completions"
type Env = {}
const auth = async (c: Context, next: Next) => {
const authHeader = c.req.header("authorization")
if (!authHeader || !authHeader.startsWith("Bearer ")) {
return c.json(
{
error: {
message: "Missing API key.",
type: "invalid_request_error",
param: null,
code: "unauthorized",
},
},
401,
)
}
const apiKey = authHeader.split(" ")[1]
// Replace with your validation logic
if (apiKey !== Resource.OPENCODE_API_KEY.value) {
return c.json(
{
error: {
message: "Invalid API key.",
type: "invalid_request_error",
param: null,
code: "unauthorized",
},
},
401,
)
}
await next()
}
export default new Hono<{ Bindings: Env }>()
.get("/", (c) => c.text("Hello, world!"))
.post("/v1/chat/completions", auth, async (c) => {
try {
const body = await c.req.json<ChatCompletionCreateParamsBase>()
console.log(body)
const model = (() => {
const [provider, ...parts] = body.model.split("/")
const model = parts.join("/")
if (provider === "anthropic" && model === "claude-sonnet-4") {
return createAnthropic({
apiKey: Resource.ANTHROPIC_API_KEY.value,
})("claude-sonnet-4-20250514")
}
if (provider === "openai" && model === "gpt-4.1") {
return createOpenAI({
apiKey: Resource.OPENAI_API_KEY.value,
})("gpt-4.1")
}
if (provider === "zhipuai" && model === "glm-4.5-flash") {
return createOpenAICompatible({
name: "Zhipu AI",
baseURL: "https://api.z.ai/api/paas/v4",
apiKey: Resource.ZHIPU_API_KEY.value,
})("glm-4.5-flash")
}
throw new Error(`Unsupported provider: ${provider}`)
})()
const requestBody = transformOpenAIRequestToAiSDK()
return body.stream ? await handleStream() : await handleGenerate()
async function handleStream() {
const result = await streamText({
model,
...requestBody,
})
const encoder = new TextEncoder()
const stream = new ReadableStream({
async start(controller) {
const id = `chatcmpl-${Date.now()}`
const created = Math.floor(Date.now() / 1000)
try {
for await (const chunk of result.fullStream) {
// TODO
//console.log("!!! CHUCK !!!", chunk);
switch (chunk.type) {
case "text-delta": {
const data = {
id,
object: "chat.completion.chunk",
created,
model: body.model,
choices: [
{
index: 0,
delta: {
content: chunk.text,
},
finish_reason: null,
},
],
}
controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
break
}
case "reasoning-delta": {
const data = {
id,
object: "chat.completion.chunk",
created,
model: body.model,
choices: [
{
index: 0,
delta: {
reasoning_content: chunk.text,
},
finish_reason: null,
},
],
}
controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
break
}
case "tool-call": {
const data = {
id,
object: "chat.completion.chunk",
created,
model: body.model,
choices: [
{
index: 0,
delta: {
tool_calls: [
{
id: chunk.toolCallId,
type: "function",
function: {
name: chunk.toolName,
arguments: JSON.stringify(chunk.input),
},
},
],
},
finish_reason: null,
},
],
}
controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
break
}
case "error": {
const data = {
id,
object: "chat.completion.chunk",
created,
model: body.model,
error: {
message: chunk.error,
type: "server_error",
},
}
controller.enqueue(encoder.encode(`data: ${JSON.stringify(data)}\n\n`))
controller.enqueue(encoder.encode("data: [DONE]\n\n"))
controller.close()
break
}
case "finish": {
const finishReason =
{
stop: "stop",
length: "length",
"content-filter": "content_filter",
"tool-calls": "tool_calls",
error: "stop",
other: "stop",
unknown: "stop",
}[chunk.finishReason] || "stop"
const data = {
id,
object: "chat.completion.chunk",
created,
model: body.model,
choices: [
{
index: 0,
delta: {},
finish_reason: finishReason,
},
],
usage: {
prompt_tokens: chunk.totalUsage.inputTokens,
completion_tokens: chunk.totalUsage.outputTokens,
total_tokens: chunk.totalUsage.totalTokens,
completion_tokens_details: {
reasoning_tokens: chunk.totalUsage.reasoningTokens,
},
prompt_tokens_details: {
cached_tokens: chunk.totalUsage.cachedInputTokens,
},
},
}
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 "start-step":
case "finish-step":
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: ${(chunk as any).type}`)
break
}
}
} catch (error) {
controller.error(error)
}
},
})
return new Response(stream, {
headers: {
"Content-Type": "text/plain; charset=utf-8",
"Cache-Control": "no-cache",
Connection: "keep-alive",
},
})
}
async function handleGenerate() {
const response = await generateText({
model,
...requestBody,
})
return c.json({
id: `chatcmpl-${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,
},
})),
},
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,
},
},
})
}
function transformOpenAIRequestToAiSDK() {
const prompt = transformMessages()
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" }
if (body.response_format.type === "json_schema")
return {
type: "json",
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" }
throw new Error("Unsupported response format")
})(),
seed: body.seed ?? undefined,
}
function transformTools() {
const { tools, tool_choice } = body
if (!tools || tools.length === 0) {
return { tools: undefined, toolChoice: undefined }
}
const aiSdkTools = tools.reduce(
(acc, tool) => {
acc[tool.function.name] = {
type: "function" as const,
name: tool.function.name,
description: tool.function.description,
inputSchema: tool.function.parameters,
}
return acc
},
{} as Record<string, any>,
)
let aiSdkToolChoice
if (tool_choice == null) {
aiSdkToolChoice = undefined
} else if (tool_choice === "auto") {
aiSdkToolChoice = "auto"
} else if (tool_choice === "none") {
aiSdkToolChoice = "none"
} else if (tool_choice === "required") {
aiSdkToolChoice = "required"
} else if (tool_choice.type === "function") {
aiSdkToolChoice = {
type: "tool",
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
}
}
} catch (error: any) {
return c.json({ error: { message: error.message } }, 500)
}
})
.all("*", (c) => c.text("Not Found"))