Function queryEmbedding

lib/queryLLM.ts:369–404

(
  textToEmbed: string | string[],
  model: string = "text-embedding-ada-002",
)

}

export async function queryEmbedding(
  textToEmbed: string | string[],
  model: string = "text-embedding-ada-002",
): 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,
      input: textToEmbed,
    }),
  });

  const responseJson: EmbeddingResponse | { error: OpenAIError } =
    await response.json();

  if (response.status === 429) {
    // Throwing an error triggers exponential backoff retry
    throw new Error(
      `OpenAI API rate limit exceeded. Full error: ${JSON.stringify(
        responseJson,
      )}`,
    );
  }
  if ("error" in responseJson) {
    throw new Error(
      "Error from embedding: " +
        JSON.stringify(responseJson.error, undefined, 2),
    );
  }

  return responseJson.data.map((item) => item.embedding);
}

function combineMessagesForHFEndpoints(messages: LLMChatMessage[]): string {
  return messages