Package dev.langchain4j.model.azure

Class AzureOpenAiLanguageModel

java.lang.Object

dev.langchain4j.model.azure.AzureOpenAiLanguageModel

All Implemented Interfaces:

LanguageModel, TokenCountEstimator

public class AzureOpenAiLanguageModel
extends Object
implements LanguageModel, TokenCountEstimator

Represents an OpenAI language model, hosted on Azure, such as gpt-3.5-turbo-instruct. However, it's recommended to use AzureOpenAiChatModel instead, as it offers more advanced features like function calling, multi-turn conversations, etc.

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())`.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	AzureOpenAiLanguageModel.Builder

Constructor Summary

Constructors

Constructor	Description
AzureOpenAiLanguageModel(com.azure.ai.openai.OpenAIClient client, String deploymentName, Tokenizer tokenizer, Integer maxTokens, Double temperature, Double topP, Map<String,Integer> logitBias, String user, Integer n, Integer logprobs, Boolean echo, List<String> stop, Double presencePenalty, Double frequencyPenalty, Integer bestOf)
AzureOpenAiLanguageModel(String endpoint, String serviceVersion, com.azure.core.credential.KeyCredential keyCredential, String deploymentName, Tokenizer tokenizer, Integer maxTokens, Double temperature, Double topP, Map<String,Integer> logitBias, String user, Integer n, Integer logprobs, Boolean echo, List<String> stop, Double presencePenalty, Double frequencyPenalty, Integer bestOf, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses, String userAgentSuffix, Map<String,String> customHeaders)
AzureOpenAiLanguageModel(String endpoint, String serviceVersion, com.azure.core.credential.TokenCredential tokenCredential, String deploymentName, Tokenizer tokenizer, Integer maxTokens, Double temperature, Double topP, Map<String,Integer> logitBias, String user, Integer n, Integer logprobs, Boolean echo, List<String> stop, Double presencePenalty, Double frequencyPenalty, Integer bestOf, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses, String userAgentSuffix, Map<String,String> customHeaders)
AzureOpenAiLanguageModel(String endpoint, String serviceVersion, String apiKey, String deploymentName, Tokenizer tokenizer, Integer maxTokens, Double temperature, Double topP, Map<String,Integer> logitBias, String user, Integer n, Integer logprobs, Boolean echo, List<String> stop, Double presencePenalty, Double frequencyPenalty, Integer bestOf, Duration timeout, Integer maxRetries, com.azure.core.http.ProxyOptions proxyOptions, boolean logRequestsAndResponses, String userAgentSuffix, Map<String,String> customHeaders)

Method Summary

Modifier and Type	Method	Description
static AzureOpenAiLanguageModel.Builder	builder()
int	estimateTokenCount(String prompt)	Estimates the count of tokens in the given text.
Response<String>	generate(String prompt)	Generate a response to the given prompt.

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.language.LanguageModel

generate

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

estimateTokenCount, estimateTokenCount

Constructor Details

AzureOpenAiLanguageModel

public AzureOpenAiLanguageModel(com.azure.ai.openai.OpenAIClient client,
 String deploymentName,
 Tokenizer tokenizer,
 Integer maxTokens,
 Double temperature,
 Double topP,
 Map<String,Integer> logitBias,
 String user,
 Integer n,
 Integer logprobs,
 Boolean echo,
 List<String> stop,
 Double presencePenalty,
 Double frequencyPenalty,
 Integer bestOf)

AzureOpenAiLanguageModel

public AzureOpenAiLanguageModel(String endpoint,
 String serviceVersion,
 String apiKey,
 String deploymentName,
 Tokenizer tokenizer,
 Integer maxTokens,
 Double temperature,
 Double topP,
 Map<String,Integer> logitBias,
 String user,
 Integer n,
 Integer logprobs,
 Boolean echo,
 List<String> stop,
 Double presencePenalty,
 Double frequencyPenalty,
 Integer bestOf,
 Duration timeout,
 Integer maxRetries,
 com.azure.core.http.ProxyOptions proxyOptions,
 boolean logRequestsAndResponses,
 String userAgentSuffix,
 Map<String,String> customHeaders)

AzureOpenAiLanguageModel

public AzureOpenAiLanguageModel(String endpoint,
 String serviceVersion,
 com.azure.core.credential.KeyCredential keyCredential,
 String deploymentName,
 Tokenizer tokenizer,
 Integer maxTokens,
 Double temperature,
 Double topP,
 Map<String,Integer> logitBias,
 String user,
 Integer n,
 Integer logprobs,
 Boolean echo,
 List<String> stop,
 Double presencePenalty,
 Double frequencyPenalty,
 Integer bestOf,
 Duration timeout,
 Integer maxRetries,
 com.azure.core.http.ProxyOptions proxyOptions,
 boolean logRequestsAndResponses,
 String userAgentSuffix,
 Map<String,String> customHeaders)

AzureOpenAiLanguageModel

public AzureOpenAiLanguageModel(String endpoint,
 String serviceVersion,
 com.azure.core.credential.TokenCredential tokenCredential,
 String deploymentName,
 Tokenizer tokenizer,
 Integer maxTokens,
 Double temperature,
 Double topP,
 Map<String,Integer> logitBias,
 String user,
 Integer n,
 Integer logprobs,
 Boolean echo,
 List<String> stop,
 Double presencePenalty,
 Double frequencyPenalty,
 Integer bestOf,
 Duration timeout,
 Integer maxRetries,
 com.azure.core.http.ProxyOptions proxyOptions,
 boolean logRequestsAndResponses,
 String userAgentSuffix,
 Map<String,String> customHeaders)

Method Details

generate

public Response<String> generate(String prompt)

Description copied from interface: LanguageModel

Generate a response to the given prompt.

Specified by:

generate in interface LanguageModel

Parameters:

prompt - the prompt.

Returns:

the response.

estimateTokenCount

public int estimateTokenCount(String prompt)

Description copied from interface: TokenCountEstimator

Estimates the count of tokens in the given text.

Specified by:

estimateTokenCount in interface TokenCountEstimator

Parameters:

prompt - the text.

Returns:

the estimated count of tokens.

builder

public static AzureOpenAiLanguageModel.Builder builder()