Package dev.langchain4j.model.azure

Class AzureOpenAiEmbeddingModel

java.lang.Object
dev.langchain4j.model.embedding.DimensionAwareEmbeddingModel
dev.langchain4j.model.azure.AzureOpenAiEmbeddingModel
All Implemented Interfaces:
EmbeddingModel, TokenCountEstimator
public class AzureOpenAiEmbeddingModel extends DimensionAwareEmbeddingModel implements TokenCountEstimator
Represents an OpenAI embedding model, hosted on Azure, such as text-embedding-ada-002.

Mandatory parameters for initialization are: endpoint and apikey (or an alternate authentication method, see below for more information). Optionally you can set serviceVersion (if not, the latest version is used) and deploymentName (if not, a default name is used). You can also provide your own OpenAIClient instance, if you need more flexibility.

There are 3 authentication methods:

1. Azure OpenAI API Key Authentication: this is the most common method, using an Azure OpenAI API key. You need to provide the OpenAI API Key as a parameter, using the apiKey() method in the Builder, or the apiKey parameter in the constructor: For example, you would use `builder.apiKey("{key}")`.

2. non-Azure OpenAI API Key Authentication: this method allows to use the OpenAI service, instead of Azure OpenAI. You can use the nonAzureApiKey() method in the Builder, which will also automatically set the endpoint to "https://api.openai.com/v1". For example, you would use `builder.nonAzureApiKey("{key}")`. The constructor requires a KeyCredential instance, which can be created using `new AzureKeyCredential("{key}")`, and doesn't set up the endpoint.

3. Azure OpenAI client with Microsoft Entra ID (formerly Azure Active Directory) credentials. - This requires to add the `com.azure:azure-identity` dependency to your project, which is an optional dependency to this library. - You need to provide a TokenCredential instance, using the tokenCredential() method in the Builder, or the tokenCredential parameter in the constructor. As an example, DefaultAzureCredential can be used to authenticate the client: Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET. Then, provide the DefaultAzureCredential instance to the builder: `builder.tokenCredential(new DefaultAzureCredentialBuilder().build())`.

  • Constructor Details

    • AzureOpenAiEmbeddingModel

      public AzureOpenAiEmbeddingModel(String endpoint, String serviceVersion, String apiKey, String deploymentName, Tokenizer tokenizer, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses, String userAgentSuffix, Integer dimensions, Map<String,String> customHeaders)

    • AzureOpenAiEmbeddingModel

      public AzureOpenAiEmbeddingModel(String endpoint, String serviceVersion, com.azure.core.credential.KeyCredential keyCredential, String deploymentName, Tokenizer tokenizer, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses, String userAgentSuffix, Integer dimensions, Map<String,String> customHeaders)

    • AzureOpenAiEmbeddingModel

      public AzureOpenAiEmbeddingModel(String endpoint, String serviceVersion, com.azure.core.credential.TokenCredential tokenCredential, String deploymentName, Tokenizer tokenizer, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses, String userAgentSuffix, Integer dimensions, Map<String,String> customHeaders)

  • Method Details