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fix: replace AiGateway.embedBatch with direct LiteLLM cURL for upload indexing

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AiGateway uses getenv(LITELLM_MASTER_KEY) + stream_context HTTP which was
failing on the chloe virtualhost process. New dbnToolsLiteLLMEmbedBatch()
helper mirrors dbnToolsCallGpuLlm — hardcoded URL + key, cURL-first, same
pattern already proven for LLM calls. Removes AiGateway dependency from
DeepResearchAgent entirely.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
davegilligan
2026-05-15 11:30:25 +02:00
parent e130db8119
commit 3196c33ebb
2 changed files with 71 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
includes/DeepResearchAgent.php
+2 -5
View File
@@ -14,7 +14,6 @@ final class DbnDeepResearchAgent
private const POOL_CAP = 30;
private DbnAzureOpenAiGateway $azure;
private ?AiGateway $ai = null;
private array $uploadVecs = [];
private array $stepTimings = [];
@@ -50,9 +49,7 @@ final class DbnDeepResearchAgent
dbnToolsBootCaveau();
$aiPortalRoot = dbnToolsAiPortalRoot();
require_once $aiPortalRoot . '/platform/includes/dbn_v6.php';
require_once $aiPortalRoot . '/lib/ai/AiGateway.php';
$this->ai = new AiGateway();
$this->uploadVecs = [];
$this->stepTimings = [];
@@ -141,7 +138,7 @@ final class DbnDeepResearchAgent
if ($uploadChunks) {
try {
$texts = array_map(fn(array $c) => $c['text'], $uploadChunks);
$vecs = $this->ai->embedBatch($texts, 'nomic-embed-text');
$vecs = dbnToolsLiteLLMEmbedBatch($texts);
if (count($vecs) === count($uploadChunks)) {
foreach ($uploadChunks as $i => $chunk) {
$this->uploadVecs[] = [
@@ -531,7 +528,7 @@ PROMPT;
return [];
}
try {
$qVec = $this->ai->embed($question, 'nomic-embed-text');
$qVec = dbnToolsLiteLLMEmbedBatch([$question])[0] ?? [];
} catch (Throwable $e) {
error_log('DBN deep research sub-Q embed failed: ' . $e->getMessage());
return [];
includes/bootstrap.php
+69
View File
@@ -676,3 +676,72 @@ function dbnToolsCallGpuLlm(array $messages, array $options = []): array
}
return $decoded;
}
/**
* Batch-embed texts via LiteLLM /v1/embeddings using cURL.
* Returns an array of float[] indexed by input position.
*/
function dbnToolsLiteLLMEmbedBatch(array $texts, string $model = 'nomic-embed-text', int $timeout = 60): array
{
if (empty($texts)) {
return [];
}
$url = 'http://10.0.1.10:4000/v1/embeddings';
$apiKey = (string)(dbnToolsEnv('LITELLM_MASTER_KEY') ?: 'sk-bnl-litellm-26xR9mK4qvN3wL8sTj7pB2d');
$payload = json_encode(['model' => $model, 'input' => array_values($texts)], JSON_UNESCAPED_UNICODE);
$headers = [
'Content-Type: application/json',
'Authorization: Bearer ' . $apiKey,
];
if (function_exists('curl_init')) {
$ch = curl_init($url);
curl_setopt_array($ch, [
CURLOPT_RETURNTRANSFER => true,
CURLOPT_POST => true,
CURLOPT_POSTFIELDS => $payload,
CURLOPT_HTTPHEADER => $headers,
CURLOPT_TIMEOUT => $timeout,
]);
$response = curl_exec($ch);
$code = (int)curl_getinfo($ch, CURLINFO_RESPONSE_CODE);
$err = curl_error($ch);
curl_close($ch);
if ($response === false) {
throw new RuntimeException('LiteLLM embed request failed: ' . $err);
}
} else {
$ctx = stream_context_create(['http' => [
'method' => 'POST',
'header' => implode("\r\n", $headers),
'content' => $payload,
'timeout' => $timeout,
'ignore_errors' => true,
]]);
$response = @file_get_contents($url, false, $ctx);
$code = 0;
if (isset($http_response_header[0]) && preg_match('/\s(\d{3})\s/', $http_response_header[0], $m)) {
$code = (int)$m[1];
}
if ($response === false) {
throw new RuntimeException('LiteLLM embed request failed.');
}
}
$decoded = json_decode($response, true);
if (!is_array($decoded)) {
throw new RuntimeException('LiteLLM embed returned non-JSON.');
}
if ($code < 200 || $code >= 300) {
$msg = $decoded['error']['message'] ?? ('HTTP ' . $code);
throw new RuntimeException('LiteLLM embed error: ' . $msg);
}
// OpenAI /v1/embeddings returns data[].embedding ordered by index
$data = $decoded['data'] ?? [];
usort($data, fn($a, $b) => ($a['index'] ?? 0) <=> ($b['index'] ?? 0));
return array_map(fn($d) => $d['embedding'], $data);
}