/**
 * Azure OpenAI text embeddings.
 *
 * Reuses the same Azure OpenAI resource as the agent (see agent/agent.ts). We
 * use the `text-embedding-3-small` deployment (1536 dimensions) to turn chat
 * memories and queries into vectors for Cosmos DB vector search.
 */
import { createAzure } from "@ai-sdk/azure";
import { embed, embedMany } from "ai";

/** Must match the vector embedding policy dimensions on the memory container. */
export const EMBEDDING_DIMENSIONS = 1536;

function embeddingModel() {
  const endpoint = (process.env.AZURE_OPENAI_ENDPOINT ?? "").replace(/\/$/, "");
  if (!endpoint) {
    throw new Error("AZURE_OPENAI_ENDPOINT is not set.");
  }
  const azure = createAzure({
    baseURL: `${endpoint}/openai`,
    apiKey: process.env.AZURE_OPENAI_API_KEY,
    apiVersion: process.env.AZURE_OPENAI_API_VERSION ?? "preview",
  });
  const deployment =
    process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT ?? "text-embedding-3-small";
  return azure.textEmbeddingModel(deployment);
}

/** Embed a single string into a 1536-dim vector. */
export async function embedText(text: string): Promise<number[]> {
  const { embedding } = await embed({ model: embeddingModel(), value: text });
  return embedding;
}

/** Embed many strings in one request (fewer round-trips than looping). */
export async function embedTexts(texts: string[]): Promise<number[][]> {
  if (texts.length === 0) return [];
  const { embeddings } = await embedMany({
    model: embeddingModel(),
    values: texts,
  });
  return embeddings;
}