总结expert领域的功能
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wangjinlei
219
application/common/service/LocalModelService.php
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219
application/common/service/LocalModelService.php
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<?php
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namespace app\common\service;
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use think\Env;
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/**
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* 本地模型服务：按上下文长度自动选择模型
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*
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* - 短上下文 -> 小模型（显存为大模型一半），对应 base.model_url1
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* - 长上下文 -> 大模型，对应 base.model_url
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*
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* 选择规则：上下文字符数 <= 阈值 用小模型；超过阈值 用大模型。
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* 两个端点模型名相同（base.model）。
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*
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* 用法：
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* $svc = new LocalModelService();
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* $res = $svc->chat([
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* ['role' => 'system', 'content' => '...'],
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* ['role' => 'user', 'content' => '...'],
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* ]);
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* // $res['ok'], $res['content'], $res['tier'](small|large), $res['context_len']
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*
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* // 只要文本结果：
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* $text = $svc->complete($systemPrompt, $userPrompt);
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*/
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class LocalModelService
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{
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/** 上下文长度阈值（字符数）：<= 用小模型，> 用大模型 */
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const CONTEXT_THRESHOLD = 3000;
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/** 请求超时（秒） */
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const TIMEOUT = 120;
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/** 小模型端点（短上下文，显存一半） */
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private $smallUrl;
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/** 大模型端点（长上下文） */
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private $largeUrl;
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/** 模型名（两端点相同） */
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private $model;
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/** 上下文长度阈值（字符数） */
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private $threshold;
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public function __construct()
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{
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// 小模型 -> base.model_url1，大模型 -> base.model_url，模型名同为 base.model
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$this->smallUrl = $this->normalizeChatUrl((string)Env::get('base.model_url1', ''));
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$this->largeUrl = $this->normalizeChatUrl((string)Env::get('base.model_url', ''));
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$this->model = trim((string)Env::get('base.model', ''));
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$this->threshold = self::CONTEXT_THRESHOLD;
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}
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/**
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* 发起一次对话，按上下文长度自动选模型。
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*
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* @param array $messages OpenAI 格式 messages
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* @param array $options 可选：
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* - temperature (float, 默认 0.2)
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* - max_tokens (int, 可选)
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* - force_tier ('small'|'large') 强制指定模型，跳过长度判断
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* - extra (array) 透传到请求体的额外字段
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* @return array{ok:bool, content:string, tier:string, model:string, url:string, context_len:int, error:string}
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*/
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public function chat(array $messages, array $options = [])
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{
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$contextLen = $this->measureMessages($messages);
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$tier = isset($options['force_tier']) && in_array($options['force_tier'], ['small', 'large'], true)
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? $options['force_tier']
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: $this->pickTier($contextLen);
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$endpoint = $this->resolveEndpoint($tier);
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$result = [
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'ok' => false,
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'content' => '',
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'tier' => $tier,
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'model' => $endpoint['model'],
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'url' => $endpoint['url'],
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'context_len' => $contextLen,
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'error' => '',
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];
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if ($endpoint['url'] === '' || $endpoint['model'] === '') {
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$result['error'] = $tier . ' 模型未配置（检查 .env [base] model_url / model_url1 / model）';
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return $result;
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}
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$payload = [
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'model' => $endpoint['model'],
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'temperature' => isset($options['temperature']) ? (float)$options['temperature'] : 0.2,
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'messages' => $messages,
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];
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if (isset($options['max_tokens']) && intval($options['max_tokens']) > 0) {
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$payload['max_tokens'] = intval($options['max_tokens']);
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}
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if (isset($options['extra']) && is_array($options['extra'])) {
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$payload = array_merge($payload, $options['extra']);
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}
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$content = $this->postChat($endpoint['url'], $payload, $err);
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if ($content === null) {
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$result['error'] = $err !== '' ? $err : 'LLM 请求失败';
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return $result;
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}
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$result['ok'] = true;
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$result['content'] = $content;
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return $result;
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}
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/**
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* 便捷方法：传 system + user，返回纯文本内容（失败返回空字符串）。
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*/
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public function complete($systemPrompt, $userPrompt, array $options = [])
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{
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$messages = [];
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if (trim((string)$systemPrompt) !== '') {
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$messages[] = ['role' => 'system', 'content' => (string)$systemPrompt];
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}
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$messages[] = ['role' => 'user', 'content' => (string)$userPrompt];
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$res = $this->chat($messages, $options);
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return $res['ok'] ? $res['content'] : '';
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}
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/**
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* 根据上下文长度选择 tier。
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*/
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public function pickTier($contextLen)
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{
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return $contextLen > $this->threshold ? 'large' : 'small';
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}
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/**
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* 统计 messages 的上下文长度（所有 content 字符数之和）。
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*/
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public function measureMessages(array $messages)
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{
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$len = 0;
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foreach ($messages as $m) {
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if (isset($m['content']) && is_string($m['content'])) {
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$len += mb_strlen($m['content']);
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}
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}
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return $len;
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}
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/**
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* 返回某 tier 的端点配置（模型名两端点相同）。
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*/
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private function resolveEndpoint($tier)
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{
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$url = $tier === 'large' ? $this->largeUrl : $this->smallUrl;
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return ['url' => $url, 'model' => $this->model];
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}
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private function postChat($url, array $payload, &$err = '')
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{
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$err = '';
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$ch = curl_init();
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curl_setopt($ch, CURLOPT_URL, $url);
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curl_setopt($ch, CURLOPT_POST, true);
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curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($payload, JSON_UNESCAPED_UNICODE));
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curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
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curl_setopt($ch, CURLOPT_SSL_VERIFYPEER, false);
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curl_setopt($ch, CURLOPT_CONNECTTIMEOUT, 15);
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curl_setopt($ch, CURLOPT_TIMEOUT, self::TIMEOUT);
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$headers = ['Content-Type: application/json'];
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curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
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$raw = curl_exec($ch);
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if ($raw === false) {
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$err = 'curl error: ' . curl_error($ch);
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curl_close($ch);
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return null;
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}
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$httpCode = intval(curl_getinfo($ch, CURLINFO_HTTP_CODE));
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curl_close($ch);
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if ($httpCode < 200 || $httpCode >= 300) {
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$err = 'http ' . $httpCode . ': ' . mb_substr((string)$raw, 0, 300);
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return null;
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}
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$data = json_decode($raw, true);
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if (!is_array($data)) {
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$err = 'invalid json response';
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return null;
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}
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if (isset($data['choices'][0]['message']['content'])) {
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return (string)$data['choices'][0]['message']['content'];
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}
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if (isset($data['content'])) {
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return (string)$data['content'];
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}
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$err = 'no content in response: ' . mb_substr((string)$raw, 0, 300);
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return null;
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}
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/**
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* 根地址自动补 /v1/chat/completions。
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*/
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private function normalizeChatUrl($url)
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{
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$url = trim((string)$url);
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if ($url === '') {
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return '';
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}
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if (stripos($url, 'chat/completions') !== false) {
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return $url;
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}
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return rtrim($url, '/') . '/v1/chat/completions';
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}
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}
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