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DeepInfra Embeddings

The DeepInfraEmbeddings class utilizes the DeepInfra API to generate embeddings for given text inputs. This guide will walk you through the setup and usage of the DeepInfraEmbeddings class, helping you integrate it into your project seamlessly.

Installation​

Install the @langchain/community package as shown below:

tip

See this section for general instructions on installing integration packages.

npm i @langchain/community @langchain/core

Initialization​

With this integration, you can use the DeepInfra embeddings model to get embeddings for your text data. Here is the link to the embeddings models.

First, you need to sign up on the DeepInfra website and get the API token from here. You can copy names from the model cards and start using them in your code.

To use the DeepInfraEmbeddings class, you need an API token from DeepInfra. You can pass this token directly to the constructor or set it as an environment variable (DEEPINFRA_API_TOKEN).

Basic Usage​

Here’s how to create an instance of DeepInfraEmbeddings:

import { DeepInfraEmbeddings } from "@langchain/community/embeddings/deepinfra";

const embeddings = new DeepInfraEmbeddings({
  apiToken: "YOUR_API_TOKEN",
  modelName: "sentence-transformers/clip-ViT-B-32", // Optional, defaults to "sentence-transformers/clip-ViT-B-32"
  batchSize: 1024, // Optional, defaults to 1024
});

If the apiToken is not provided, it will be read from the DEEPINFRA_API_TOKEN environment variable.

Generating Embeddings​

Embedding a Single Query​

To generate embeddings for a single text query, use the embedQuery method:

const embedding = await embeddings.embedQuery(
  "What would be a good company name for a company that makes colorful socks?"
);
console.log(embedding);

Embedding Multiple Documents​

To generate embeddings for multiple documents, use the embedDocuments method. This method will handle batching automatically based on the batchSize parameter:

const documents = [
  "Document 1 text...",
  "Document 2 text...",
  "Document 3 text...",
];

const embeddingsArray = await embeddings.embedDocuments(documents);
console.log(embeddingsArray);

Customizing Requests​

You can customize the base URL the SDK sends requests to by passing a configuration parameter:

const customEmbeddings = new DeepInfraEmbeddings({
  apiToken: "YOUR_API_TOKEN",
  configuration: {
    baseURL: "https://your_custom_url.com",
  },
});

This allows you to route requests through a custom endpoint if needed.

Error Handling​

If the API token is not provided and cannot be found in the environment variables, an error will be thrown:

try {
  const embeddings = new DeepInfraEmbeddings();
} catch (error) {
  console.error("DeepInfra API token not found");
}

Example​

Here’s a complete example of how to set up and use the DeepInfraEmbeddings class:

import { DeepInfraEmbeddings } from "@langchain/community/embeddings/deepinfra";

const embeddings = new DeepInfraEmbeddings({
  apiToken: "YOUR_API_TOKEN",
  modelName: "sentence-transformers/clip-ViT-B-32",
  batchSize: 512,
});

async function runExample() {
  const queryEmbedding = await embeddings.embedQuery("Example query text.");
  console.log("Query Embedding:", queryEmbedding);

  const documents = ["Text 1", "Text 2", "Text 3"];
  const documentEmbeddings = await embeddings.embedDocuments(documents);
  console.log("Document Embeddings:", documentEmbeddings);
}

runExample();

Feedback and Support​

For feedback or questions, please contact feedback@deepinfra.com.

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You can also leave detailed feedback on GitHub.