Class AzureOpenAiEmbeddingModel
- All Implemented Interfaces:
EmbeddingModel
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())`.
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Nested Class Summary
Nested Classes -
Field Summary
Fields inherited from class dev.langchain4j.model.embedding.DimensionAwareEmbeddingModel
dimension
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Constructor Summary
ConstructorsConstructorDescriptionAzureOpenAiEmbeddingModel
(String endpoint, String serviceVersion, com.azure.core.credential.KeyCredential keyCredential, String deploymentName, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses, String userAgentSuffix, Integer dimensions, Map<String, String> customHeaders) AzureOpenAiEmbeddingModel
(String endpoint, String serviceVersion, com.azure.core.credential.TokenCredential tokenCredential, String deploymentName, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses, String userAgentSuffix, Integer dimensions, Map<String, String> customHeaders) AzureOpenAiEmbeddingModel
(String endpoint, String serviceVersion, String apiKey, String deploymentName, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses, String userAgentSuffix, Integer dimensions, Map<String, String> customHeaders) -
Method Summary
Modifier and TypeMethodDescriptionbuilder()
embedAll
(List<TextSegment> textSegments) Embeds the provided text segments, processing a maximum of 16 segments at a time.protected Integer
When known (e.g., can be derived from the model name), returns the dimension of theEmbedding
produced by this embedding model.Methods inherited from class dev.langchain4j.model.embedding.DimensionAwareEmbeddingModel
dimension
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
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Constructor Details
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AzureOpenAiEmbeddingModel
public AzureOpenAiEmbeddingModel(String endpoint, String serviceVersion, String apiKey, String deploymentName, 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, 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, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses, String userAgentSuffix, Integer dimensions, Map<String, String> customHeaders)
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Method Details
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embedAll
Embeds the provided text segments, processing a maximum of 16 segments at a time. For more information, refer to the documentation here.- Parameters:
textSegments
- A list of text segments.- Returns:
- A list of corresponding embeddings.
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builder
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knownDimension
Description copied from class:DimensionAwareEmbeddingModel
When known (e.g., can be derived from the model name), returns the dimension of theEmbedding
produced by this embedding model. Otherwise, it returnsnull
.- Overrides:
knownDimension
in classDimensionAwareEmbeddingModel
- Returns:
- the known dimension of the
Embedding
, ornull
if unknown.
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