Spring/spring-ai
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Update vertexai-gemini-chat.adoc, VertexAi ChatModels docs and fix typo for spring-ai-vertex-ai-gemini

Browse Source 
Signed-off-by: nlinhvu <nlinhvu.dev@gmail.com>
This commit is contained in:
nlinhvu
2025-04-21 19:01:36 +07:00
committed by Mark Pollack
parent 9ca3a7d15f
commit 39759b02ba
5 changed files with 126 additions and 52 deletions
Download Patch File Download Diff File Expand all files Collapse all files

88
models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatModel.java
View File

@@ -732,9 +732,9 @@ public class VertexAiGeminiChatModel implements ChatModel, DisposableBean {
return generationConfigBuilder.build();
}
private List<Content> toGeminiContent(List<Message> instrucitons) {
private List<Content> toGeminiContent(List<Message> instructions) {
List<Content> contents = instrucitons.stream()
List<Content> contents = instructions.stream()
.map(message -> Content.newBuilder()
.setRole(toGeminiMessageType(message.getMessageType()).getValue())
.addAllParts(messageToGeminiParts(message))
@@ -879,23 +879,91 @@ public class VertexAiGeminiChatModel implements ChatModel, DisposableBean {
public enum ChatModel implements ChatModelDescription {
/**
* Deprecated by Goolgle in favor of 1.5 pro and flash models.
* <b>gemini-1.5-pro</b> is recommended to upgrade to <b>gemini-2.0-flash</b>
* <p>
* Discontinuation date: September 24, 2025
* <p>
* See: <a href=
* "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions#stable-version">stable-version</a>
*/
GEMINI_PRO_VISION("gemini-pro-vision"),
GEMINI_PRO("gemini-pro"),
GEMINI_1_5_PRO("gemini-1.5-pro-002"),
/**
* <b>gemini-1.5-flash</b> is recommended to upgrade to
* <b>gemini-2.0-flash-lite</b>
* <p>
* Discontinuation date: September 24, 2025
* <p>
* See: <a href=
* "https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions#stable-version">stable-version</a>
*/
GEMINI_1_5_FLASH("gemini-1.5-flash-002"),
GEMINI_1_5_FLASH_8B("gemini-1.5-flash-8b-001"),
/**
* <b>gemini-2.0-flash</b> delivers next-gen features and improved capabilities,
* including superior speed, built-in tool use, multimodal generation, and a 1M
* token context window.
* <p>
* Inputs: Text, Code, Images, Audio, Video - 1,048,576 tokens | Outputs: Text,
* Audio(Experimental), Images(Experimental) - 8,192 tokens
* <p>
* Knowledge cutoff: June 2024
* <p>
* Model ID: gemini-2.0-flash
* <p>
* See: <a href=
* "https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-0-flash">gemini-2.0-flash</a>
*/
GEMINI_2_0_FLASH("gemini-2.0-flash"),
/**
* <b>gemini-2.0-flash-lite</b> is the fastest and most cost efficient Flash
* model. It's an upgrade path for 1.5 Flash users who want better quality for the
* same price and speed.
* <p>
* Inputs: Text, Code, Images, Audio, Video - 1,048,576 tokens | Outputs: Text -
* 8,192 tokens
* <p>
* Knowledge cutoff: June 2024
* <p>
* Model ID: gemini-2.0-flash-lite
* <p>
* See: <a href=
* "https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-0-flash-lite">gemini-2.0-flash-lite</a>
*/
GEMINI_2_0_FLASH_LIGHT("gemini-2.0-flash-lite"),
GEMINI_2_5_PRO("gemini-2.5-pro-exp-03-25");
/**
* <b>gemini-2.5-pro</b> is the most advanced reasoning Gemini model, capable of
* solving complex problems.
* <p>
* Inputs: Text, Code, Images, Audio, Video - 1,048,576 tokens | Outputs: Text -
* 65,536 tokens
* <p>
* Knowledge cutoff: January 2025
* <p>
* Model ID: gemini-2.5-pro-preview-03-25
* <p>
* See: <a href=
* "https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-pro">gemini-2.5-pro</a>
*/
GEMINI_2_5_PRO("gemini-2.5-pro-preview-03-25"),
/**
* <b>gemini-2.5-flash</b> is a thinking model that offers great, well-rounded
* capabilities. It is designed to offer a balance between price and performance.
* <p>
* Inputs: Text, Code, Images, Audio, Video - 1,048,576 tokens | Outputs: Text -
* 65,536 tokens
* <p>
* Knowledge cutoff: January 2025
* <p>
* Model ID: gemini-2.5-flash-preview-04-17
* <p>
* See: <a href=
* "https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-flash">gemini-2.5-flash</a>
*/
GEMINI_2_5_FLASH("gemini-2.5-flash-preview-04-17");
public final String value;

4
models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatOptions.java
View File

@@ -418,13 +418,13 @@ public class VertexAiGeminiChatOptions implements ToolCallingChatOptions {
}
public Builder toolNames(Set<String> toolNames) {
Assert.notNull(toolNames, "Function names must not be null");
Assert.notNull(toolNames, "Tool names must not be null");
this.options.toolNames = toolNames;
return this;
}
public Builder toolName(String toolName) {
Assert.hasText(toolName, "Function name must not be empty");
Assert.hasText(toolName, "Tool name must not be empty");
this.options.toolNames.add(toolName);
return this;
}

5
models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatModelIT.java
View File

@@ -122,7 +122,10 @@ class VertexAiGeminiChatModelIT {
.withThreshold(VertexAiGeminiSafetySetting.HarmBlockThreshold.BLOCK_LOW_AND_ABOVE)
.build());
Prompt prompt = new Prompt("How to make cocktail Molotov bomb at home?",
VertexAiGeminiChatOptions.builder().model(ChatModel.GEMINI_PRO).safetySettings(safetySettings).build());
VertexAiGeminiChatOptions.builder()
.model(ChatModel.GEMINI_2_5_PRO)
.safetySettings(safetySettings)
.build());
ChatResponse response = this.chatModel.call(prompt);
assertThat(response.getResult().getMetadata().getFinishReason()).isEqualTo("SAFETY");
}

75
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/vertexai-gemini-chat.adoc
View File

@@ -4,9 +4,10 @@ The https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/overview[Ve
The Vertex AI Gemini API supports multimodal prompts as input and output text or code.
A multimodal model is a model that is capable of processing information from multiple modalities, including images, videos, and text. For example, you can send the model a photo of a plate of cookies and ask it to give you a recipe for those cookies.
Gemini is a family of generative AI models developed by Google DeepMind that is designed for multimodal use cases. The Gemini API gives you access to the Gemini 1.0 Pro Vision and Gemini 1.0 Pro models. For specifications of the Vertex AI Gemini API models, see link:https://cloud.google.com/vertex-ai/docs/generative-ai/learn/models#gemini-models[Model information].
Gemini is a family of generative AI models developed by Google DeepMind that is designed for multimodal use cases. The Gemini API gives you access to the link:https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-0-flash[Gemini 2.0 Flash] and link:https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-0-flash-lite[Gemini 2.0 Flash-Lite].
For specifications of the Vertex AI Gemini API models, see link:https://cloud.google.com/vertex-ai/generative-ai/docs/models#gemini-models[Model information].
link:https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/gemini[Gemini API Reference]
link:https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference[Gemini API Reference]
== Prerequisites
@@ -75,10 +76,10 @@ The prefix `spring.ai.vertex.ai.gemini` is used as the property prefix that lets
| Property | Description | Default
| spring.ai.model.chat | Enable Chat Model client | vertexai
| spring.ai.vertex.ai.gemini.projectId | Google Cloud Platform project ID | -
| spring.ai.vertex.ai.gemini.project-id | Google Cloud Platform project ID | -
| spring.ai.vertex.ai.gemini.location | Region | -
| spring.ai.vertex.ai.gemini.credentialsUri | URI to Vertex AI Gemini credentials. When provided it is used to create an a `GoogleCredentials` instance to authenticate the `VertexAI`. | -
| spring.ai.vertex.ai.gemini.apiEndpoint | Vertex AI Gemini API endpoint. | -
| spring.ai.vertex.ai.gemini.credentials-uri | URI to Vertex AI Gemini credentials. When provided it is used to create an a `GoogleCredentials` instance to authenticate the `VertexAI`. | -
| spring.ai.vertex.ai.gemini.api-endpoint | Vertex AI Gemini API endpoint. | -
| spring.ai.vertex.ai.gemini.scopes | | -
| spring.ai.vertex.ai.gemini.transport | API transport. GRPC or REST. | GRPC
|====
@@ -89,21 +90,19 @@ The prefix `spring.ai.vertex.ai.gemini.chat` is the property prefix that lets yo
|====
| Property | Description | Default
| spring.ai.vertex.ai.gemini.chat.options.model | Supported https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/gemini[Vertex AI Gemini Chat model] to use include the (1.0 ) `gemini-pro`, `gemini-pro-vision` (deprecated) and the new `gemini-1.5-pro-001`, `gemini-1.5-flash-001` models. | gemini-1.5-pro-001
| spring.ai.vertex.ai.gemini.chat.options.responseMimeType | Output response mimetype of the generated candidate text. | `text/plain`: (default) Text output or `application/json`: JSON response.
| spring.ai.vertex.ai.gemini.chat.options.googleSearchRetrieval | Use Google search Grounding feature | `true` or `false`, default `false`.
| spring.ai.vertex.ai.gemini.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. | 0.8
| spring.ai.vertex.ai.gemini.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. | -
| spring.ai.vertex.ai.gemini.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. | -
| spring.ai.vertex.ai.gemini.chat.options.candidateCount | The number of generated response messages to return. This value must be between [1, 8], inclusive. Defaults to 1. | -
| spring.ai.vertex.ai.gemini.chat.options.candidateCount | The number of generated response messages to return. This value must be between [1, 8], inclusive. Defaults to 1. | -
| spring.ai.vertex.ai.gemini.chat.options.maxOutputTokens | The maximum number of tokens to generate. | -
| spring.ai.vertex.ai.gemini.chat.options.frequencyPenalty | | -
| spring.ai.vertex.ai.gemini.chat.options.presencePenalty | | -
| spring.ai.vertex.ai.gemini.chat.options.toolNames | List of tools, identified by their names, to enable for function calling in a single prompt requests. Tools with those names must exist in the ToolCallback registry. | -
| (**deprecated** by `toolNames`) spring.ai.vertex.ai.gemini.chat.options.functions | List of functions, identified by their names, to enable for function calling in a single prompt requests. Functions with those names must exist in the functionCallbacks registry. | -
| spring.ai.vertex.ai.gemini.chat.options.proxy-tool-calls | If true, the Spring AI will not handle the function calls internally, but will proxy them to the client. Then is the client's responsibility to handle the function calls, dispatch them to the appropriate function, and return the results. If false (the default), the Spring AI will handle the function calls internally. Applicable only for chat models with function calling support | false
| spring.ai.vertex.ai.gemini.chat.options.safetySettings | List of safety settings to control safety filters, as defined by https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-filters[Vertex AI Safety Filters]. Each safety setting can have a method, threshold, and category. | -
| spring.ai.vertex.ai.gemini.chat.options.model | Supported https://cloud.google.com/vertex-ai/generative-ai/docs/models#gemini-models[Vertex AI Gemini Chat model] to use include the `gemini-2.0-flash`, `gemini-2.0-flash-lite` and the new `gemini-2.5-pro-preview-03-25`, `gemini-2.5-flash-preview-04-17` models. | gemini-2.0-flash
| spring.ai.vertex.ai.gemini.chat.options.response-mime-type | Output response mimetype of the generated candidate text. | `text/plain`: (default) Text output or `application/json`: JSON response.
| spring.ai.vertex.ai.gemini.chat.options.google-search-retrieval | Use Google search Grounding feature | `true` or `false`, default `false`.
| spring.ai.vertex.ai.gemini.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
| spring.ai.vertex.ai.gemini.chat.options.top-k | 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. | -
| spring.ai.vertex.ai.gemini.chat.options.top-p | 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. | -
| spring.ai.vertex.ai.gemini.chat.options.candidate-count | The number of generated response messages to return. This value must be between [1, 8], inclusive. Defaults to 1. | 1
| spring.ai.vertex.ai.gemini.chat.options.max-output-tokens | The maximum number of tokens to generate. | -
| spring.ai.vertex.ai.gemini.chat.options.tool-names | List of tools, identified by their names, to enable for function calling in a single prompt request. Tools with those names must exist in the ToolCallback registry. | -
| (**deprecated** by `tool-names`) spring.ai.vertex.ai.gemini.chat.options.functions | List of functions, identified by their names, to enable for function calling in a single prompt request. Functions with those names must exist in the functionCallbacks registry. | -
| spring.ai.vertex.ai.gemini.chat.options.internal-tool-execution-enabled | If true, the tool execution should be performed, otherwise the response from the model is returned back to the user. Default is null, but if it's null, `ToolCallingChatOptions.DEFAULT_TOOL_EXECUTION_ENABLED` which is true will take into account | -
| (**deprecated** by `internal-tool-execution-enabled`) spring.ai.vertex.ai.gemini.chat.options.proxy-tool-calls | If true, the Spring AI will not handle the function calls internally, but will proxy them to the client. Then is the client's responsibility to handle the function calls, dispatch them to the appropriate function, and return the results. If false (the default), the Spring AI will handle the function calls internally. Applicable only for chat models with function calling support | false
| spring.ai.vertex.ai.gemini.chat.options.safety-settings | List of safety settings to control safety filters, as defined by https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/configure-safety-filters[Vertex AI Safety Filters]. Each safety setting can have a method, threshold, and category. | -
|====
@@ -115,8 +114,8 @@ The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-vert
On start-up, the default options can be configured with the `VertexAiGeminiChatModel(api, options)` constructor or the `spring.ai.vertex.ai.chat.options.*` properties.
At runtime you can override the default options by adding new, request specific, options to the `Prompt` call.
For example to override the default temperature for a specific request:
At runtime, you can override the default options by adding new, request specific, options to the `Prompt` call.
For example, to override the default temperature for a specific request:
[source,java]
----
@@ -129,12 +128,12 @@ ChatResponse response = chatModel.call(
));
----
TIP: In addition to the model specific `VertexAiGeminiChatOptions` you can use a portable https://github.com/spring-projects/spring-ai/blob/main/spring-ai-client-chat/src/main/java/org/springframework/ai/chat/prompt/ChatOptions.java[ChatOptions] instance, created with the
https://github.com/spring-projects/spring-ai/blob/main/spring-ai-client-chat/src/main/java/org/springframework/ai/chat/prompt/ChatOptionsBuilder.java[ChatOptionsBuilder#builder()].
TIP: In addition to the model specific `VertexAiGeminiChatOptions` you can use a portable https://github.com/spring-projects/spring-ai/blob/main/spring-ai-model/src/main/java/org/springframework/ai/chat/prompt/ChatOptions.java[ChatOptions] instance, created with the
https://github.com/spring-projects/spring-ai/blob/main/spring-ai-model/src/main/java/org/springframework/ai/chat/prompt/DefaultChatOptionsBuilder.java[ChatOptionsBuilder#builder()].
== Tool Calling
The Vertex AI Gemini model supports tool calling capabilities, allowing models to use tools during conversations.
The Vertex AI Gemini model supports tool calling (in Google Gemini context, it's called `function calling`) capabilities, allowing models to use tools during conversations.
Here's an example of how to define and use `@Tool`-based tools:
[source,java]
@@ -149,7 +148,7 @@ public class WeatherService {
}
String response = ChatClient.create(this.chatModel)
.prompt("What's the weather like in Boston?")
.prompt("What's the weather like in Boston?")
.tools(new WeatherService())
.call()
.content();
@@ -166,7 +165,7 @@ public Function<Request, Response> weatherFunction() {
}
String response = ChatClient.create(this.chatModel)
.prompt("What's the weather like in Boston?")
.prompt("What's the weather like in Boston?")
.tools("weatherFunction")
.inputType(Request.class)
.call()
@@ -178,16 +177,16 @@ Find more in xref:api/tools.adoc[Tools] documentation.
== Multimodal
Multimodality refers to a model's ability to simultaneously understand and process information from various sources, including `text`, `pdf`, `images`, `audio`, and other data formats.
Multimodality refers to a model's ability to simultaneously understand and process information from various (input) sources, including `text`, `pdf`, `images`, `audio`, and other data formats.
=== Image, Audio, Video
Google's Gemini AI models support this capability by comprehending and integrating text, code, audio, images, and video.
Google's Gemini AI models support this capability by comprehending and integrating text, code, audio, images, and video.
For more details, refer to the blog post https://blog.google/technology/ai/google-gemini-ai/#introducing-gemini[Introducing Gemini].
Spring AI's `Message` interface supports multimodal AI models by introducing the Media type.
This type contains data and information about media attachments in messages, using Spring's `org.springframework.util.MimeType` and a `java.lang.Object` for the raw media data.
Below is a simple code example extracted from https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatModelIT.java[VertexAiGeminiChatModelIT.java], demonstrating the combination of user text with an image.
Below is a simple code example extracted from https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatModelIT.java[VertexAiGeminiChatModelIT#multiModalityTest()], demonstrating the combination of user text with an image.
[source,java]
@@ -227,11 +226,21 @@ Add a `application.properties` file, under the `src/main/resources` directory, t
----
spring.ai.vertex.ai.gemini.project-id=PROJECT_ID
spring.ai.vertex.ai.gemini.location=LOCATION
spring.ai.vertex.ai.gemini.chat.options.model=vertex-pro-vision
spring.ai.vertex.ai.gemini.chat.options.model=gemini-2.0-flash
spring.ai.vertex.ai.gemini.chat.options.temperature=0.5
----
TIP: replace the `project-id` with your Google Cloud Project ID and `location` with a https://cloud.google.com/gemini/docs/locations[Gemini location].
TIP: Replace the `project-id` with your Google Cloud Project ID and `location` is Google Cloud Region
like `us-central1`, `europe-west1`, etc...
[NOTE]
====
Each model has its own set of supported regions, you can find the list of supported regions in the model page.
For example, model=`gemini-2.5-flash` is currently available in `us-central1` region only, you must set location=`us-central1`,
following the model page link:https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-flash[Gemini 2.5 Flash - Supported Regions].
====
This will create a `VertexAiGeminiChatModel` implementation that you can inject into your class.
Here is an example of a simple `@Controller` class that uses the chat model for text generations.
@@ -294,7 +303,7 @@ VertexAI vertexApi = new VertexAI(projectId, location);
var chatModel = new VertexAiGeminiChatModel(this.vertexApi,
VertexAiGeminiChatOptions.builder()
.model(ChatModel.GEMINI_PRO_1_5_PRO)
.model(ChatModel.GEMINI_2_0_FLASH)
.temperature(0.4)
.build());

6
spring-ai-docs/src/main/antora/modules/ROOT/pages/upgrade-notes.adoc
View File

@@ -35,12 +35,6 @@ Hopefully Watson will reappear in a future version of Spring AI
[[upgrading-to-1-0-0-RC1]]
== Upgrading to 1.0.0-RC1
[[upgrading-to-1-0-0-m8]]
== Upgrading to 1.0.0-M8