Add VertexAi Chat Optios Support + Docs
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Christian Tzolov
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** xref:api/chatclient.adoc[]
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*** xref:api/clients/openai-chat.adoc[]
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*** xref:api/clients/azure-openai-chat.adoc[]
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*** xref:api/clients/ollama-chat.adoc[]
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*** xref:api/bedrock.adoc[Amazon Bedrock Chat]
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**** xref:api/clients/bedrock/bedrock-anthropic.adoc[]
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**** xref:api/clients/bedrock/bedrock-llama2.adoc[]
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**** xref:api/clients/bedrock/bedrock-cohere.adoc[]
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**** xref:api/clients/bedrock/bedrock-titan.adoc[]
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*** xref:api/clients/huggingface.adoc[]
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*** xref:api/clients/ollama-chat.adoc[]
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*** xref:api/clients/vertexai-chat.adoc[]
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** xref:api/prompt.adoc[]
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** xref:api/output-parser.adoc[]
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** xref:api/etl-pipeline.adoc[]
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= VertexAI Chat
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The link:https://developers.generativeai.google/api/rest/generativelanguage[Generative Language] PaLM API allows developers to build generative AI applications using the PaLM model. Large Language Models (LLMs) are a powerful, versatile type of machine learning model that enables computers to comprehend and generate natural language through a series of prompts. The PaLM API is based on Google's next generation LLM, PaLM. It excels at a variety of different tasks like code generation, reasoning, and writing. You can use the PaLM API to build generative AI applications for use cases like content generation, dialogue agents, summarization and classification systems, and more.
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Based on the link:https://developers.generativeai.google/api/rest/generativelanguage/models[Models REST API].
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== Prerequisites
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To access the PaLM2 REST API you need to obtain an access API KEY form link:https://makersuite.google.com/app/apikey[makersuite].
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NOTE: Currently the PaLM API it is not available outside US, but you can use VPN for testing.
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The Spring AI project defines a configuration property named `spring.ai.vertex.ai.api-key` that you should set to the value of the `API Key` obtained from openai.com.
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Exporting an environment variable is one way to set that configuration property:
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[source,shell]
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----
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export SPRING_AI_VERTEX_AI_API_KEY=<INSERT KEY HERE>
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----
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== Auto-configuration
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Spring AI provides Spring Boot auto-configuration for the VertexAI Chat Client.
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To enable it add the following dependency to your project's Maven `pom.xml` file:
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[source, xml]
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----
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-vertex-ai-spring-boot-starter</artifactId>
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<version>0.8.0-SNAPSHOT</version>
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</dependency>
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----
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or to your Gradle `build.gradle` build file.
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[source,groovy]
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----
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dependencies {
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implementation 'org.springframework.ai:spring-ai-vertex-ai-spring-boot-starter:0.8.0-SNAPSHOT'
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}
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----
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TIP: Refer to the xref:getting-started.adoc#_dependency_management[Dependency Management] section to add Milestone and/or Snapshot Repositories to your build file.
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=== Chat Properties
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The prefix `spring.ai.vertex.ai` is used as the property prefix that lets you connect to OpenAI.
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[cols="3,5,1"]
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|====
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| Property | Description | Default
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| spring.ai.vertex.ai.ai.base-url | The URL to connect to | https://generativelanguage.googleapis.com/v1beta3
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| spring.ai.vertex.ai.api-key | The API Key | -
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|====
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The prefix `spring.ai.vertex.ai.chat` is the property prefix that lets you configure the chat client implementation for VertexAI Chat.
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[cols="3,5,1"]
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|====
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| Property | Description | Default
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| spring.ai.vertex.ai.chat.model | This is the https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text-chat[Vertex Chat model] to use | chat-bison-001
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| spring.ai.vertex.ai.chat.options.temperature | Controls the randomness of the output. Values can range over [0.0,1.0], inclusive. A value closer to 1.0 will produce responses that are more varied, while a value closer to 0.0 will typically result in less surprising responses from the generative. This value specifies default to be used by the backend while making the call to the generative. | 0.7
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| spring.ai.vertex.ai.chat.options.topK | The maximum number of tokens to consider when sampling. The generative uses combined Top-k and nucleus sampling. Top-k sampling considers the set of topK most probable tokens. | 20
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| spring.ai.vertex.ai.chat.options.topP | The maximum cumulative probability of tokens to consider when sampling. The generative uses combined Top-k and nucleus sampling. Nucleus sampling considers the smallest set of tokens whose probability sum is at least topP. | -
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| spring.ai.vertex.ai.chat.options.candidateCount | The number of generated response messages to return. This value must be between [1, 8], inclusive. Defaults to 1. | 1
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|====
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TIP: All properties prefixed with `spring.ai.vertex.ai.chat.options` can be overridden at runtime by adding a request specific <<chat-options>> to the `Prompt` call.
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=== Chat Options [[chat-options]]
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The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/vertex/VertexAiChatOptions.java[VertexAiChatOptions.java] provides model configurations, such as the temperature, the topK, etc.
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On start-up, the default options can be configured with the `VertexAiChatClient(api, options)` constructor or the `spring.ai.vertex.ai.chat.options.*` properties.
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At run-time you can override the default options by adding new, request specific, options to the `Prompt` call.
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For example to override the default temperature for a specific request:
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[source,java]
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----
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ChatResponse response = chatClient.call(
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new Prompt(
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"Generate the names of 5 famous pirates.",
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VertexAiChatOptions.builder()
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.withTemperature(0.4)
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.build()
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));
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----
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TIP: In addition to the model specific `VertexAiChatOptions` you can use a portable https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/ChatOptions.java[ChatOptions] instance, created with the https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/chat/ChatOptionsBuilder.java[ChatOptionsBuilder#builder()].
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=== Sample Controller (Auto-configuration)
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https://start.spring.io/[Create] a new Spring Boot project and add the `spring-ai-openai-spring-boot-starter` to your pom (or gradle) dependencies.
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Add a `application.properties` file, under the `src/main/resources` directory, to enable and configure the VertexAi Chat client:
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[source,application.properties]
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----
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spring.ai.vertex.ai.api-key=YOUR_API_KEY
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spring.ai.vertex.ai.chat.model=chat-bison-001
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spring.ai.vertex.ai.chat.options.temperature=0.5
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----
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TIP: replace the `api-key` with your VertexAI credentials.
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This will create a `VertexAiChatClient` implementation that you can inject into your class.
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Here is an example of a simple `@Controller` class that uses the chat client for text generations.
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[source,java]
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----
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@RestController
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public class ChatController {
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private final VertexAiChatClient chatClient;
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@Autowired
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public ChatController(VertexAiChatClient chatClient) {
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this.chatClient = chatClient;
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}
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@GetMapping("/open-ai/generate")
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public Map generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
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return Map.of("generation", chatClient.call(message));
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}
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@GetMapping("/open-ai/generateStream")
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public Flux<ChatResponse> generateStream(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) {
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Prompt prompt = new Prompt(new UserMessage(message));
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return chatClient.stream(prompt);
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}
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}
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----
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== Manual Configuration
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The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/vertex/VertexAiChatClient.java[OpenAiChatClient] implements the `ChatClient` and uses the <<low-level-api>> to connect to the VertexAI service.
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Add the `spring-ai-vertex-ai` dependency to your project's Maven `pom.xml` file:
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[source, xml]
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----
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<dependency>
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<groupId>org.springframework.ai</groupId>
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<artifactId>spring-ai-vertex-ai</artifactId>
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<version>0.8.0-SNAPSHOT</version>
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</dependency>
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----
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or to your Gradle `build.gradle` build file.
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[source,groovy]
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----
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dependencies {
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implementation 'org.springframework.ai:spring-ai-vertex-ai:0.8.0-SNAPSHOT'
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}
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----
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TIP: Refer to the xref:getting-started.adoc#_dependency_management[Dependency Management] section to add Milestone and/or Snapshot Repositories to your build file.
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Next, create a `VertexAiChatClient` and use it for text generations:
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[source,java]
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----
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VertexAiApi vertexAiApi = new VertexAiApi(< YOUR PALM_API_KEY>);
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var chatClient = new VertexAiChatClient(vertexAiApi,
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VertexAiChatOptions.builder()
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.withTemperature(0.4)
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.build());
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ChatResponse response = chatClient.call(
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new Prompt("Generate the names of 5 famous pirates."));
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----
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The `VertexAiChatOptions` provides the configuration information for the chat requests.
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The `VertexAiChatOptions.Builder` is fluent options builder.
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=== Low-level VertexAiApi Client [[low-level-api]]
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The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/vertex/api/VertexAiApi.java[VertexAiApi] provides is lightweight Java client for VertexAiApi Chat API.
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Following class diagram illustrates the `VertexAiApi` chat interfaces and building blocks:
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image::vertex-ai-chat-low-level-api.jpg[w=800,align="center"]
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Here is a simple snippet how to use the api programmatically:
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[source,java]
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----
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VertexAiApi vertexAiApi = new VertexAiApi(< YOUR PALM_API_KEY>);
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// Generate
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var prompt = new MessagePrompt(List.of(new Message("0", "Hello, how are you?")));
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GenerateMessageRequest request = new GenerateMessageRequest(prompt);
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GenerateMessageResponse response = vertexAiApi.generateMessage(request);
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// Embed text
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Embedding embedding = vertexAiApi.embedText("Hello, how are you?");
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// Batch embedding
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List<Embedding> embeddings = vertexAiApi.batchEmbedText(List.of("Hello, how are you?", "I am fine, thank you!"));
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----
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