asepharyana
100b62800c
Backend uses extensionless @/ alias imports; the double .js caused ERR_MODULE_NOT_FOUND at runtime (index.js.js).
44 lines
1.4 KiB
TypeScript
44 lines
1.4 KiB
TypeScript
import { config } from "@/shared/config/index";
|
|
import { createChildLogger } from "@/shared/logger/index";
|
|
|
|
const logger = createChildLogger("messages-embed");
|
|
|
|
/**
|
|
* Embed a search query with the configured OpenAI-compatible embedding model.
|
|
* Uses raw fetch (the backend has no openai SDK dependency) and returns null
|
|
* when embeddings are not configured (search unavailable).
|
|
*
|
|
* encoding_format: "float" is REQUIRED — Nvidia-backed models reject base64.
|
|
*/
|
|
export async function embedQuery(text: string): Promise<number[] | null> {
|
|
if (!config.AI_LLM_API_KEY || !config.AI_LLM_EMBEDDING_MODEL) return null;
|
|
try {
|
|
const res = await fetch(`${config.AI_LLM_BASE_URL}/embeddings`, {
|
|
method: "POST",
|
|
headers: {
|
|
"Content-Type": "application/json",
|
|
Authorization: `Bearer ${config.AI_LLM_API_KEY}`,
|
|
},
|
|
body: JSON.stringify({
|
|
model: config.AI_LLM_EMBEDDING_MODEL,
|
|
input: text,
|
|
encoding_format: "float",
|
|
}),
|
|
});
|
|
if (!res.ok) {
|
|
logger.warn({ status: res.status }, "query embed HTTP error");
|
|
return null;
|
|
}
|
|
const json = (await res.json()) as {
|
|
data?: Array<{ embedding?: number[] }>;
|
|
};
|
|
return json.data?.[0]?.embedding ?? null;
|
|
} catch (error) {
|
|
logger.warn(
|
|
{ error: error instanceof Error ? error.message : String(error) },
|
|
"query embed failed",
|
|
);
|
|
return null;
|
|
}
|
|
}
|