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Readme: Generate embeddings using OpenAI with node.js

The JavaScript demo in this repository is used to create vectorized data that can be indexed in a search index. There are no calls to Cognitive Search, but it does call Azure OpenAI. You'll need access to Azure OpenAI in your Azure subscription to run this demo.

Prerequisites

To run this code, you will need the following:

  • An Azure subscription, with access to Azure OpenAI

  • A deployment of the text-embedding-ada-002 embedding model in your Azure OpenAI service. We use API version 2022-12-01 in this demo. For the deployment name, we used the same name as the model, "text-embedding-ada-002".

  • Azure OpenAI connection and model information:

    • OpenAI API key
    • OpenAI embedding model deployment name
    • OpenAI API version

  • Node.js (these instructions were tested with version Node.js version 16.0)

You can use Visual Studio Code with the JavaScript extension for this demo. For help setting up the environment, see this JavaScript quickstart.

You don't need Azure Cognitive Search for this step.

Setup

  1. Clone this repository.

  2. Create a .env file in the same directory as the code and include the following variables:

    OPENAI_SERVICE_NAME=YOUR-OPENAI-SERVICE-NAME
DEPLOYMENT_NAME=YOUR-MODEL-DEPLOYMENT-NAME
OPENAI_API_VERSION=YOUR-OPENAI-API-VERSION

Run the Code

To run the code, execute the following command from a command line:

For document vectorization, run the following command: node docs-text-openai-embeddings.js

This will generate embeddings for the title and content fields of the input data and write the embeddings to the docVectors.json file in the output directory.

For query vectorization, run the following command: node query-text-openai-embedding.js

Output

The code writes the input_data with the added embeddings and "@search.action" field to the docVectors.json file in the output directory. The embeddings can be uploaded to an Azure Cognitive Search index using the 2023-07-01-preview API version of the Add, Update, or Delete Documents REST API.

You can also generate a query embedding to perform vector searches.