dev.langchain4j.model.azure

Class AzureOpenAiEmbeddingModel

java.lang.Object

dev.langchain4j.model.azure.AzureOpenAiEmbeddingModel

All Implemented Interfaces:

dev.langchain4j.model.embedding.EmbeddingModel, dev.langchain4j.model.embedding.TokenCountEstimator

public class AzureOpenAiEmbeddingModel
extends Object
implements dev.langchain4j.model.embedding.EmbeddingModel, dev.langchain4j.model.embedding.TokenCountEstimator

Represents an OpenAI embedding model, hosted on Azure, such as text-embedding-ada-002.

Mandatory parameters for initialization are: endpoint, serviceVersion, apikey (or an alternate authentication method, see below for more information) and deploymentName. 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())`.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	AzureOpenAiEmbeddingModel.Builder

Constructor Summary

Constructors

Constructor and Description
AzureOpenAiEmbeddingModel(String endpoint, String serviceVersion, com.azure.core.credential.KeyCredential keyCredential, String deploymentName, dev.langchain4j.model.Tokenizer tokenizer, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses)
AzureOpenAiEmbeddingModel(String endpoint, String serviceVersion, String apiKey, String deploymentName, dev.langchain4j.model.Tokenizer tokenizer, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses)
AzureOpenAiEmbeddingModel(String endpoint, String serviceVersion, com.azure.core.credential.TokenCredential tokenCredential, String deploymentName, dev.langchain4j.model.Tokenizer tokenizer, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses)

Method Summary

Modifier and Type	Method and Description
static AzureOpenAiEmbeddingModel.Builder	builder()
dev.langchain4j.model.output.Response<List<dev.langchain4j.data.embedding.Embedding>>	embedAll(List<dev.langchain4j.data.segment.TextSegment> textSegments) Embeds the provided text segments, processing a maximum of 16 segments at a time.
int	estimateTokenCount(String text)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface dev.langchain4j.model.embedding.EmbeddingModel

embed, embed

Methods inherited from interface dev.langchain4j.model.embedding.TokenCountEstimator

estimateTokenCount, estimateTokenCount

Constructor Detail

AzureOpenAiEmbeddingModel

public AzureOpenAiEmbeddingModel(String endpoint,
                                 String serviceVersion,
                                 String apiKey,
                                 String deploymentName,
                                 dev.langchain4j.model.Tokenizer tokenizer,
                                 Duration timeout,
                                 Integer maxRetries,
                                 com.azure.core.http.ProxyOptions proxyOptions,
                                 boolean logRequestsAndResponses)

AzureOpenAiEmbeddingModel

public AzureOpenAiEmbeddingModel(String endpoint,
                                 String serviceVersion,
                                 com.azure.core.credential.KeyCredential keyCredential,
                                 String deploymentName,
                                 dev.langchain4j.model.Tokenizer tokenizer,
                                 Duration timeout,
                                 Integer maxRetries,
                                 com.azure.core.http.ProxyOptions proxyOptions,
                                 boolean logRequestsAndResponses)

AzureOpenAiEmbeddingModel

public AzureOpenAiEmbeddingModel(String endpoint,
                                 String serviceVersion,
                                 com.azure.core.credential.TokenCredential tokenCredential,
                                 String deploymentName,
                                 dev.langchain4j.model.Tokenizer tokenizer,
                                 Duration timeout,
                                 Integer maxRetries,
                                 com.azure.core.http.ProxyOptions proxyOptions,
                                 boolean logRequestsAndResponses)

Method Detail

embedAll

public dev.langchain4j.model.output.Response<List<dev.langchain4j.data.embedding.Embedding>> embedAll(List<dev.langchain4j.data.segment.TextSegment> textSegments)

Embeds the provided text segments, processing a maximum of 16 segments at a time. For more information, refer to the documentation here.

Specified by:

embedAll in interface dev.langchain4j.model.embedding.EmbeddingModel

Parameters:

textSegments - A list of text segments.

Returns:

A list of corresponding embeddings.

estimateTokenCount

public int estimateTokenCount(String text)

Specified by:

estimateTokenCount in interface dev.langchain4j.model.embedding.TokenCountEstimator

builder

public static AzureOpenAiEmbeddingModel.Builder builder()