Additional Functionality: Embeddings

We offer a number of additional features for chat models. In the examples below, we'll be using the ChatOpenAI model.

Adding a timeout

By default, LangChain will wait indefinitely for a response from the model provider. If you want to add a timeout, you can pass a timeout option, in milliseconds, when you instantiate the model. For example, for OpenAI:

import { OpenAIEmbeddings } from "langchain/embeddings/openai";

export const run = async () => {
  const embeddings = new OpenAIEmbeddings({
    timeout: 1000, // 1s timeout
  });
  /* Embed queries */
  const res = await embeddings.embedQuery("Hello world");
  console.log(res);
  /* Embed documents */
  const documentRes = await embeddings.embedDocuments([
    "Hello world",
    "Bye bye",
  ]);
  console.log({ documentRes });
};

Currently, the timeout option is only supported for OpenAI models.

Dealing with Rate Limits

Some providers have rate limits. If you exceed the rate limit, you'll get an error. To help you deal with this, LangChain provides a maxConcurrency option when instantiating an Embeddings model. This option allows you to specify the maximum number of concurrent requests you want to make to the provider. If you exceed this number, LangChain will automatically queue up your requests to be sent as previous requests complete.

For example, if you set maxConcurrency: 5, then LangChain will only send 5 requests to the provider at a time. If you send 10 requests, the first 5 will be sent immediately, and the next 5 will be queued up. Once one of the first 5 requests completes, the next request in the queue will be sent.

To use this feature, simply pass maxConcurrency: <number> when you instantiate the LLM. For example:

import { OpenAIEmbeddings } from "langchain/embeddings/openai";

const model = new OpenAIEmbeddings({ maxConcurrency: 5 });

Dealing with API Errors

If the model provider returns an error from their API, by default LangChain will retry up to 6 times on an exponential backoff. This enables error recovery without any additional effort from you. If you want to change this behavior, you can pass a maxRetries option when you instantiate the model. For example:

import { OpenAIEmbeddings } from "langchain/embeddings/openai";

const model = new OpenAIEmbeddings({ maxRetries: 10 });