Configure database, model, embedding, and optional Redis settings.

# MemoGrafter stores graph memory in PostgreSQL.
DATABASE_URL=postgres://postgres:postgres@localhost:5432/memo_grafter
# Add only the providers your adapters use.
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GEMINI_API_KEY=...
# Required only for queue mode or recall caching.
REDIS_URL=redis://localhost:6379

declare const process: {
  env: {
    DATABASE_URL?: string;
    OPENAI_API_KEY?: string;
    MEMO_GRAFTER_EMBEDDING_MODEL?: string;
    REDIS_URL?: string;
  };
};
const embeddingModel =
  process.env.MEMO_GRAFTER_EMBEDDING_MODEL ?? "text-embedding-3-small";
export default {
  db: {
    connectionString: process.env.DATABASE_URL,
  },
  // Optional recall cache. Falls back to PostgreSQL if Redis is unavailable.
  // cache: process.env.REDIS_URL
  //   ? { connectionString: process.env.REDIS_URL }
  //   : undefined,
  // Optional Redis-backed ingestion; failed enqueues do not retry synchronously.
  // queue: process.env.REDIS_URL
  //   ? { redisUrl: process.env.REDIS_URL }
  //   : undefined,
  // Set OPENAI_API_KEY or replace this object with your own embedder.
  embedder: process.env.OPENAI_API_KEY
    ? {
        async embed(text: string): Promise<number[]> {
          const response = await fetch(
            "https://api.openai.com/v1/embeddings",
            {
              method: "POST",
              headers: {
                "content-type": "application/json",
                authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
              },
              body: JSON.stringify({
                model: embeddingModel,
                input: text,
              }),
            },
          );
          if (!response.ok) {
            throw new Error(
              `OpenAI embeddings request failed: ${response.status} ${await response.text()}`,
            );
          }
          const body = (await response.json()) as {
            data?: Array<{ embedding?: number[] }>;
          };
          const embedding = body.data?.[0]?.embedding;
          if (!embedding) {
            throw new Error(
              "OpenAI embeddings response did not include an embedding.",
            );
          }
          return embedding;
        },
      }
    : undefined,
};