OpenAIEmbeddings Timeout

[ZehuaZhang]

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Example Code

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

await new OpenAIEmbeddings().embedDocuments(['Hello']);

Description

⨯ Internal error: Error: Connection error.
at OpenAI.makeRequest (./node_modules/openai/core.mjs:321:19)
at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
at async eval (./node_modules/@langchain/openai/dist/embeddings.js:278:29)
at async RetryOperation.eval [as _fn] (./node_modules/p-retry/index.js:50:12)
digest: "1734331795"
Cause: FetchError: request to https://api.openai.com/v1/embeddings failed, reason: connect ETIMEDOUT 154.85.102.30:443
at ClientRequest.eval (webpack-internal:///(action-browser)/./node_modules/node-fetch/lib/index.mjs:1510:11)
at ClientRequest.emit (node:events:514:28)
at TLSSocket.socketErrorListener (node:_http_client:495:9)
at TLSSocket.emit (node:events:526:35)
at emitErrorNT (node:internal/streams/destroy:151:8)
at emitErrorCloseNT (node:internal/streams/destroy:116:3)
at process.processTicksAndRejections (node:internal/process/task_queues:82:21) {
type: 'system',
errno: 'ETIMEDOUT',
code: 'ETIMEDOUT'
}

System Info

langchain@0.2.2 | MIT | deps: 16 | versions: 273
Typescript bindings for langchain
https://github.com/langchain-ai/langchainjs/tree/main/langchain/

keywords: llm, ai, gpt3, chain, prompt, prompt engineering, chatgpt, machine learning, ml, openai, embeddings, vectorstores

dist
.tarball: https://registry.npmjs.org/langchain/-/langchain-0.2.2.tgz
.shasum: 21605450458c77f022c88fdb7671bb82f4a9a17f
.integrity: sha512-4tt2QuwW8AXdIL8CRkQeGOCoYYH3QbLHfQ09yD0iWLV1rwUYJ8mIYFAz/+u6CB8YNEyR/HI105s4xrxFQbWa9g==
.unpackedSize: 3.4 MB

dependencies:
@langchain/core: ~0.2.0 js-tiktoken: ^1.0.12 langsmith: ~0.1.7 uuid: ^9.0.0
@langchain/openai: ~0.0.28 js-yaml: ^4.1.0 ml-distance: ^4.0.0 yaml: ^2.2.1
@langchain/textsplitters: ~0.0.0 jsonpointer: ^5.0.1 openapi-types: ^12.1.3 zod-to-json-schema: ^3.22.3
binary-extensions: ^2.2.0 langchainhub: ~0.0.8 p-retry: 4 zod: ^3.22.4

maintainers:

• nfcampos nuno@boringbits.io
• jacoblee93 jacoblee93@gmail.com
• andrewnguonly andrewnguonly@gmail.com
• davidduong david@duong.cz
• hwchase17 hw.chase.17@gmail.com
• basproul braceasproul@gmail.com

dist-tags:
latest: 0.2.2 next: 0.2.0

[dosubot[bot]]

Hello, @ZehuaZhang! I'm here to help you with bugs, questions, and becoming a contributor. If you need any assistance, feel free to ask!

To resolve the timeout issue with the OpenAIEmbeddings class from the @langchain/openai package in TypeScript, you can increase the timeout duration. By default, LangChain will wait indefinitely for a response from the model provider, but you can specify a timeout in milliseconds when you instantiate the model.

Here is an example of how to increase the timeout:

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

const embeddings = new OpenAIEmbeddings({
  timeout: 5000, // 5s timeout
});

/* Embed documents */
const res = await embeddings.embedDocuments(['Hello']);
console.log(res);

By increasing the timeout, you give the requests more time to complete, which should help resolve the timeout issue. Additionally, ensure that other parameters such as batchSize and maxConcurrency are configured appropriately for your use case, as these can also impact the performance and reliability of the requests.

For example, you can set batchSize and maxConcurrency as follows:

const embeddings = new OpenAIEmbeddings({
  timeout: 5000, // 5s timeout
  batchSize: 1,
  maxConcurrency: 2,
});

These settings can help manage the load and concurrency of the requests, potentially improving performance and reducing the likelihood of timeouts ^[1][2][3][4].

Details

About Dosu
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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.

[ZehuaZhang]

It's not about timeout, I think openAI calls are simply not invoking. 'Hello' should not be taking forever to embed.
I live in China, so I need a proxy or a reverse proxy? How do I do that?

[dosubot[bot]]

If the OpenAI calls are not invoking at all, there might be an issue with the network configuration or the way the API is being called. Here are a few steps to troubleshoot and resolve the issue:

1. Check Network Configuration:
   Ensure that your network allows outbound connections to the OpenAI API endpoint. Sometimes, firewalls or proxy settings can block these requests.

2. Set Base URL and Proxy:
   If you are behind a proxy, make sure to configure it correctly. You can set the baseURL and proxy settings in the clientConfig.

3. Verify API Key:
   Ensure that your OpenAI API key is correctly set and has the necessary permissions.

4. Debugging the Request:
   Add logging to see if the request is being made and if there are any errors returned.

Here is an example with additional logging and error handling:

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

const embeddings = new OpenAIEmbeddings({
  timeout: 5000, // 5s timeout
});

try {
  console.log("Attempting to embed documents...");
  const res = await embeddings.embedDocuments(['Hello']);
  console.log("Embedding result:", res);
} catch (error) {
  console.error("An error occurred:", error);
}

5. Check for Network Errors:
   Ensure that there are no network errors such as DNS issues, SSL certificate problems, or other connectivity issues.

6. Alternative Embedding Methods:
   If the issue persists, consider using alternative embedding methods or classes within the LangChain JS framework, such as AzureOpenAIEmbeddings, GoogleGenerativeAIEmbeddings, AlibabaTongyiEmbeddings, TogetherAIEmbeddings, or GradientEmbeddings ^{[1][2][3][4][5]}.

By following these steps, you should be able to identify and resolve the issue preventing the OpenAI calls from invoking.

Details

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.