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import express from 'express'; | |
import { fal } from '@fal-ai/client'; | |
// 从环境变量读取 Fal AI API Key | |
const FAL_KEY = process.env.FAL_KEY; | |
if (!FAL_KEY) { | |
console.error("Error: FAL_KEY environment variable is not set."); | |
process.exit(1); | |
} | |
// 配置 fal 客户端 | |
fal.config({ | |
credentials: FAL_KEY, | |
}); | |
const app = express(); | |
app.use(express.json({ limit: '50mb' })); | |
app.use(express.urlencoded({ extended: true, limit: '50mb' })); | |
const PORT = process.env.PORT || 3000; | |
// === 全局定义限制 === | |
const PROMPT_LIMIT = 4800; | |
const SYSTEM_PROMPT_LIMIT = 4800; | |
// === 限制定义结束 === | |
// 定义 fal-ai/any-llm 支持的模型列表 | |
const FAL_SUPPORTED_MODELS = [ | |
"anthropic/claude-3.7-sonnet", | |
"anthropic/claude-3.5-sonnet", | |
"anthropic/claude-3-5-haiku", | |
"anthropic/claude-3-haiku", | |
"google/gemini-pro-1.5", | |
"google/gemini-flash-1.5", | |
"google/gemini-flash-1.5-8b", | |
"google/gemini-2.0-flash-001", | |
"meta-llama/llama-3.2-1b-instruct", | |
"meta-llama/llama-3.2-3b-instruct", | |
"meta-llama/llama-3.1-8b-instruct", | |
"meta-llama/llama-3.1-70b-instruct", | |
"openai/gpt-4o-mini", | |
"openai/gpt-4o", | |
"deepseek/deepseek-r1", | |
"meta-llama/llama-4-maverick", | |
"meta-llama/llama-4-scout" | |
]; | |
// Helper function to get owner from model ID | |
const getOwner = (modelId) => { | |
if (modelId && modelId.includes('/')) { | |
return modelId.split('/')[0]; | |
} | |
return 'fal-ai'; | |
} | |
// GET /v1/models endpoint | |
app.get('/v1/models', (req, res) => { | |
console.log("Received request for GET /v1/models"); | |
try { | |
const modelsData = FAL_SUPPORTED_MODELS.map(modelId => ({ | |
id: modelId, object: "model", created: 1700000000, owned_by: getOwner(modelId) | |
})); | |
res.json({ object: "list", data: modelsData }); | |
console.log("Successfully returned model list."); | |
} catch (error) { | |
console.error("Error processing GET /v1/models:", error); | |
res.status(500).json({ error: "Failed to retrieve model list." }); | |
} | |
}); | |
// === 修改后的 convertMessagesToFalPrompt 函数 (System置顶 + 分隔符 + 对话历史Recency) === | |
function convertMessagesToFalPrompt(messages) { | |
let fixed_system_prompt_content = ""; | |
const conversation_message_blocks = []; | |
console.log(`Original messages count: ${messages.length}`); | |
// 1. 分离 System 消息,格式化 User/Assistant 消息 | |
for (const message of messages) { | |
let content = (message.content === null || message.content === undefined) ? "" : String(message.content); | |
switch (message.role) { | |
case 'system': | |
fixed_system_prompt_content += `System: ${content}\n\n`; | |
break; | |
case 'user': | |
conversation_message_blocks.push(`Human: ${content}\n\n`); | |
break; | |
case 'assistant': | |
conversation_message_blocks.push(`Assistant: ${content}\n\n`); | |
break; | |
default: | |
console.warn(`Unsupported role: ${message.role}`); | |
continue; | |
} | |
} | |
// 2. 截断合并后的 system 消息(如果超长) | |
if (fixed_system_prompt_content.length > SYSTEM_PROMPT_LIMIT) { | |
const originalLength = fixed_system_prompt_content.length; | |
fixed_system_prompt_content = fixed_system_prompt_content.substring(0, SYSTEM_PROMPT_LIMIT); | |
console.warn(`Combined system messages truncated from ${originalLength} to ${SYSTEM_PROMPT_LIMIT}`); | |
} | |
// 清理末尾可能多余的空白,以便后续判断和拼接 | |
fixed_system_prompt_content = fixed_system_prompt_content.trim(); | |
// 3. 计算 system_prompt 中留给对话历史的剩余空间 | |
// 注意:这里计算时要考虑分隔符可能占用的长度,但分隔符只在需要时添加 | |
// 因此先计算不含分隔符的剩余空间 | |
let space_occupied_by_fixed_system = 0; | |
if (fixed_system_prompt_content.length > 0) { | |
// 如果固定内容不为空,计算其长度 + 后面可能的分隔符的长度(如果需要) | |
// 暂时只计算内容长度,分隔符在组合时再考虑 | |
space_occupied_by_fixed_system = fixed_system_prompt_content.length + 4; // 预留 \n\n...\n\n 的长度 | |
} | |
const remaining_system_limit = Math.max(0, SYSTEM_PROMPT_LIMIT - space_occupied_by_fixed_system); | |
console.log(`Trimmed fixed system prompt length: ${fixed_system_prompt_content.length}. Approx remaining system history limit: ${remaining_system_limit}`); | |
// 4. 反向填充 User/Assistant 对话历史 | |
const prompt_history_blocks = []; | |
const system_prompt_history_blocks = []; | |
let current_prompt_length = 0; | |
let current_system_history_length = 0; | |
let promptFull = false; | |
let systemHistoryFull = (remaining_system_limit <= 0); | |
console.log(`Processing ${conversation_message_blocks.length} user/assistant messages for recency filling.`); | |
for (let i = conversation_message_blocks.length - 1; i >= 0; i--) { | |
const message_block = conversation_message_blocks[i]; | |
const block_length = message_block.length; | |
if (promptFull && systemHistoryFull) { | |
console.log(`Both prompt and system history slots full. Omitting older messages from index ${i}.`); | |
break; | |
} | |
// 优先尝试放入 prompt | |
if (!promptFull) { | |
if (current_prompt_length + block_length <= PROMPT_LIMIT) { | |
prompt_history_blocks.unshift(message_block); | |
current_prompt_length += block_length; | |
continue; | |
} else { | |
promptFull = true; | |
console.log(`Prompt limit (${PROMPT_LIMIT}) reached. Trying system history slot.`); | |
} | |
} | |
// 如果 prompt 满了,尝试放入 system_prompt 的剩余空间 | |
if (!systemHistoryFull) { | |
if (current_system_history_length + block_length <= remaining_system_limit) { | |
system_prompt_history_blocks.unshift(message_block); | |
current_system_history_length += block_length; | |
continue; | |
} else { | |
systemHistoryFull = true; | |
console.log(`System history limit (${remaining_system_limit}) reached.`); | |
} | |
} | |
} | |
// 5. *** 组合最终的 prompt 和 system_prompt (包含分隔符逻辑) *** | |
const system_prompt_history_content = system_prompt_history_blocks.join('').trim(); | |
const final_prompt = prompt_history_blocks.join('').trim(); | |
// 定义分隔符 | |
const SEPARATOR = "\n\n-------下面是比较早之前的对话内容-----\n\n"; | |
let final_system_prompt = ""; | |
// 检查各部分是否有内容 (使用 trim 后的固定部分) | |
const hasFixedSystem = fixed_system_prompt_content.length > 0; | |
const hasSystemHistory = system_prompt_history_content.length > 0; | |
if (hasFixedSystem && hasSystemHistory) { | |
// 两部分都有,用分隔符连接 | |
final_system_prompt = fixed_system_prompt_content + SEPARATOR + system_prompt_history_content; | |
console.log("Combining fixed system prompt and history with separator."); | |
} else if (hasFixedSystem) { | |
// 只有固定部分 | |
final_system_prompt = fixed_system_prompt_content; | |
console.log("Using only fixed system prompt."); | |
} else if (hasSystemHistory) { | |
// 只有历史部分 (固定部分为空) | |
final_system_prompt = system_prompt_history_content; | |
console.log("Using only history in system prompt slot."); | |
} | |
// 如果两部分都为空,final_system_prompt 保持空字符串 "" | |
// 6. 返回结果 | |
const result = { | |
system_prompt: final_system_prompt, // 最终结果不需要再 trim | |
prompt: final_prompt // final_prompt 在组合前已 trim | |
}; | |
console.log(`Final system_prompt length (Sys+Separator+Hist): ${result.system_prompt.length}`); | |
console.log(`Final prompt length (Hist): ${result.prompt.length}`); | |
return result; | |
} | |
// === convertMessagesToFalPrompt 函数结束 === | |
// POST /v1/chat/completions endpoint (保持不变) | |
app.post('/v1/chat/completions', async (req, res) => { | |
const { model, messages, stream = false, reasoning = false, ...restOpenAIParams } = req.body; | |
console.log(`Received chat completion request for model: ${model}, stream: ${stream}`); | |
if (!FAL_SUPPORTED_MODELS.includes(model)) { | |
console.warn(`Warning: Requested model '${model}' is not in the explicitly supported list.`); | |
} | |
if (!model || !messages || !Array.isArray(messages) || messages.length === 0) { | |
console.error("Invalid request parameters:", { model, messages: Array.isArray(messages) ? messages.length : typeof messages }); | |
return res.status(400).json({ error: 'Missing or invalid parameters: model and messages array are required.' }); | |
} | |
try { | |
// *** 使用更新后的转换函数 *** | |
const { prompt, system_prompt } = convertMessagesToFalPrompt(messages); | |
const falInput = { | |
model: model, | |
prompt: prompt, | |
...(system_prompt && { system_prompt: system_prompt }), | |
reasoning: !!reasoning, | |
}; | |
console.log("Fal Input:", JSON.stringify(falInput, null, 2)); | |
console.log("Forwarding request to fal-ai with system-priority + separator + recency input:"); | |
console.log("System Prompt Length:", system_prompt?.length || 0); | |
console.log("Prompt Length:", prompt?.length || 0); | |
// 调试时取消注释可以查看具体内容 | |
console.log("--- System Prompt Start ---"); | |
console.log(system_prompt); | |
console.log("--- System Prompt End ---"); | |
console.log("--- Prompt Start ---"); | |
console.log(prompt); | |
console.log("--- Prompt End ---"); | |
// --- 流式/非流式处理逻辑 (保持不变) --- | |
if (stream) { | |
// ... 流式代码 ... | |
res.setHeader('Content-Type', 'text/event-stream; charset=utf-8'); | |
res.setHeader('Cache-Control', 'no-cache'); | |
res.setHeader('Connection', 'keep-alive'); | |
res.setHeader('Access-Control-Allow-Origin', '*'); | |
res.flushHeaders(); | |
let previousOutput = ''; | |
const falStream = await fal.stream("fal-ai/any-llm", { input: falInput }); | |
try { | |
for await (const event of falStream) { | |
const currentOutput = (event && typeof event.output === 'string') ? event.output : ''; | |
const isPartial = (event && typeof event.partial === 'boolean') ? event.partial : true; | |
const errorInfo = (event && event.error) ? event.error : null; | |
if (errorInfo) { | |
console.error("Error received in fal stream event:", errorInfo); | |
const errorChunk = { id: `chatcmpl-${Date.now()}-error`, object: "chat.completion.chunk", created: Math.floor(Date.now() / 1000), model: model, choices: [{ index: 0, delta: {}, finish_reason: "error", message: { role: 'assistant', content: `Fal Stream Error: ${JSON.stringify(errorInfo)}` } }] }; | |
res.write(`data: ${JSON.stringify(errorChunk)}\n\n`); | |
break; | |
} | |
let deltaContent = ''; | |
if (currentOutput.startsWith(previousOutput)) { | |
deltaContent = currentOutput.substring(previousOutput.length); | |
} else if (currentOutput.length > 0) { | |
console.warn("Fal stream output mismatch detected. Sending full current output as delta.", { previousLength: previousOutput.length, currentLength: currentOutput.length }); | |
deltaContent = currentOutput; | |
previousOutput = ''; | |
} | |
previousOutput = currentOutput; | |
if (deltaContent || !isPartial) { | |
const openAIChunk = { id: `chatcmpl-${Date.now()}`, object: "chat.completion.chunk", created: Math.floor(Date.now() / 1000), model: model, choices: [{ index: 0, delta: { content: deltaContent }, finish_reason: isPartial === false ? "stop" : null }] }; | |
res.write(`data: ${JSON.stringify(openAIChunk)}\n\n`); | |
} | |
} | |
res.write(`data: [DONE]\n\n`); | |
res.end(); | |
console.log("Stream finished."); | |
} catch (streamError) { | |
console.error('Error during fal stream processing loop:', streamError); | |
try { | |
const errorDetails = (streamError instanceof Error) ? streamError.message : JSON.stringify(streamError); | |
res.write(`data: ${JSON.stringify({ error: { message: "Stream processing error", type: "proxy_error", details: errorDetails } })}\n\n`); | |
res.write(`data: [DONE]\n\n`); | |
res.end(); | |
} catch (finalError) { | |
console.error('Error sending stream error message to client:', finalError); | |
if (!res.writableEnded) { res.end(); } | |
} | |
} | |
} else { | |
// --- 非流式处理 (保持不变) --- | |
console.log("Executing non-stream request..."); | |
const result = await fal.subscribe("fal-ai/any-llm", { input: falInput, logs: true }); | |
console.log("Received non-stream result from fal-ai:", JSON.stringify(result, null, 2)); | |
if (result && result.error) { | |
console.error("Fal-ai returned an error in non-stream mode:", result.error); | |
return res.status(500).json({ object: "error", message: `Fal-ai error: ${JSON.stringify(result.error)}`, type: "fal_ai_error", param: null, code: null }); | |
} | |
const openAIResponse = { | |
id: `chatcmpl-${result.requestId || Date.now()}`, object: "chat.completion", created: Math.floor(Date.now() / 1000), model: model, | |
choices: [{ index: 0, message: { role: "assistant", content: result.output || "" }, finish_reason: "stop" }], | |
usage: { prompt_tokens: null, completion_tokens: null, total_tokens: null }, system_fingerprint: null, | |
...(result.reasoning && { fal_reasoning: result.reasoning }), | |
}; | |
res.json(openAIResponse); | |
console.log("Returned non-stream response."); | |
} | |
} catch (error) { | |
console.error('Unhandled error in /v1/chat/completions:', error); | |
if (!res.headersSent) { | |
const errorMessage = (error instanceof Error) ? error.message : JSON.stringify(error); | |
res.status(500).json({ error: 'Internal Server Error in Proxy', details: errorMessage }); | |
} else if (!res.writableEnded) { | |
console.error("Headers already sent, ending response."); | |
res.end(); | |
} | |
} | |
}); | |
// 需要修改成嘅版本 (加咗 '0.0.0.0') | |
app.listen(PORT, '0.0.0.0', () => { | |
console.log(`===================================================`); | |
console.log(` Fal OpenAI Proxy Server (System Top + Separator + Recency)`); | |
console.log(` Listening on host 0.0.0.0, port: ${PORT}`); // <-- 最好改埋 log 方便確認 | |
console.log(` Using Limits: System Prompt=${SYSTEM_PROMPT_LIMIT}, Prompt=${PROMPT_LIMIT}`); | |
console.log(` Fal AI Key Loaded: ${FAL_KEY ? 'Yes' : 'No'}`); | |
console.log(` Chat Completions Endpoint: POST http://localhost:${PORT}/v1/chat/completions`); // Log 顯示 localhost 冇問題,實際監聽係 0.0.0.0 | |
console.log(` Models Endpoint: GET http://localhost:${PORT}/v1/models`); | |
console.log(`===================================================`); | |
}); | |
// 根路径响应 (更新信息) | |
app.get('/', (req, res) => { | |
res.send('Fal OpenAI Proxy (System Top + Separator + Recency Strategy) is running.'); | |
}); | |